54,741 results on '"ELECTRIC transformers"'
Search Results
2. Energy production in tollgates by the utilization of uni-directional speed breakers.
- Author
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Kumar, Ashok, Devi, Geetha, Veeramani, Kanpur Rani, and Ponraj, Ranjana
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ELECTRIC transformers , *ELECTRICAL energy , *MECHANICAL energy , *ELECTRIC power distribution grids , *ENERGY consumption - Abstract
Tollgates are part of our life. There are hundreds of cars passing by a tollgate in a day. The main objective of the project is to bring innovation in tollgates and produce energy, utilize them for other beneficial purpose. The innovation done here is the introduction of Uni-directional speed breakers, these breakers when rotated are connected to certain components which convert the mechanical rotational energy into electrical energy. The electrical energy thus produced is either stepped up by using a transformer and fed to the electric grid or is used up by the working stations near the tollgate. This project also helps in the harnessing in the waste energy.it meets up with the energy demand of the nation as the need for it keep on increasing on daily basis. [ABSTRACT FROM AUTHOR]
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- 2024
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3. A novel ZVS full-bridge cascaded step-up DC-DC converter with resonant auxiliary circuit for high voltage-gain applications.
- Author
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Hossain, M. Zakir, Selvaraj, Jeyraj A/L, and Rahim, N. A.
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DC-to-DC converters , *HIGH voltages , *ZERO voltage switching , *ELECTRIC transformers , *VOLTAGE multipliers - Abstract
High conversion ratio dc-dc converters have received significant attention in renewable energy systems, primarily due to their necessary high-gain characteristics. This research proposes a high step-up ratio full-bridge resonant cascaded (FBRC) dc-dc converter designed for use in photovoltaics (PV), fuel cells (FC), electric vehicles (EV), and other low-voltage output energy sectors to achieve high voltage gain. This converter contains a full-bridge cell with a boost input inductor, a diode-capacitor cascaded stage that replaces the transformer as a voltage multiplier and an inductor-capacitor (LC) parallel-series resonant network across the FB terminal. One of the strategic features of the converter is its high voltage step-up characteristic combined with lower duty cycle operation that limits the maximum current through the active devices, making it particularly suitable for systems that generate low output voltage. In addition, zero-voltage switching (ZVS) is achieved during the turn-off and turn-on operation of the FB switches from 25% to full load, thereby lessening the switching losses. Moreover, the diminished necessity for passive components and the decreased voltage stress on both active and passive devices lead to the use of smaller and more cost-effective components. The theoretical analysis of the proposed converter is validated using a 500 W laboratory-scale prototype wherein high-performance SiC-based MOSFETs have been utilized as switching devices. It offers reduced ripples, with input current ripple at 5% and output voltage ripple at 0.76%. When the load is 400 W and 60 V as the input voltage, the maximum efficiency is found 95.8% at 400 V output voltage. The proposed dc-dc converter, with its high voltage gain and reduced component stress, shows significant promise for application in renewable energy systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Application of Cluster Technique for Loss Estimation in Distribution Feeders via Limited Measurement Data.
- Author
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Aazami, R., Jabbar, A. Kareem, and Shirkhani, M.
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POWER factor measurement ,ELECTRIC power distribution ,ELECTRIC transformers ,EVOLUTIONARY algorithms ,ITERATIVE methods (Mathematics) - Abstract
To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders. [ABSTRACT FROM AUTHOR]
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- 2024
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5. A semi‐automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves.
- Author
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Hernandez, Andrew M., Alizadeh, Ramsey, Ghatpande, Omkar, Van Sant, Amy, and Jung, Youngkyoo
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ENERGY consumption , *GREENHOUSE gases , *MAGNETIC resonance imaging , *ELECTRIC transformers , *VOLTAGE references , *SCANNING systems - Abstract
Background and purpose Methods Results Conclusions Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi‐automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time.An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three‐phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor‐provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared‐to‐scan, idle, low‐power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1‐month LDC, for each nonproductive system mode (i.e., prepared‐to‐scan, idle, lower‐power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data).The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends.A semi‐automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. The methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners. [ABSTRACT FROM AUTHOR]
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- 2024
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6. A developed DQ control method for shunt active power filter to improve power quality in transformers.
- Author
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Al-Gahtani, Saad F., Elbarbary, Z. M. S., and Irshad, Shaik Mohammad
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ELECTRIC power filters , *POWER transformers , *ELECTRIC transformers , *HARMONIC suppression filters , *HEAT losses , *ROBUST control - Abstract
Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks. Harmonic mitigation filters with robust control technique are required to reduce the harmonic effects on power transformer. Traditionally, synchronous reference frame (DQ) is employed to control the shunt active power filter (APF) for mitigation of harmonics in power transformers. DQ control of Shunt APF lacks merits of fast response, delayed operation due to phased lock loop under abnormal grid conditions leading to insufficient harmonic elimination. A developed DQ method based on detecting the positive and negative sequence components is proposed to precisely control the shunt APF for reliable operation of power transformer. This detection technique improves the response time, mitigate the harmonics effecting the operation of transformer and overall power factor. The proposed control system is evaluated under different abnormal operating scenarios and compared with traditional DQ method. The results and analysis confirm the efficacy of the developed DQ method in improving the power transformer performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. ENERGY HARVESTING CONTROL OF CURRENT TRANSFORMERS BASED ON DYNAMIC IMPEDANCE REGULATION STRATEGY OF VOLTAGE SOURCE CONVERTER.
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Runming He, Junfeng Shi, Baoming Gao, Yu Wang, Zhenzhong Yan, and Liting Yan
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ENERGY harvesting , *ELECTRIC transformers , *CURRENT transformers (Instrument transformer) , *ELECTRIC lines , *SINE waves , *IDEAL sources (Electric circuits) - Abstract
In a wide range of current changes, it is difficult for current transformer to carry out steady energy extraction, because the excitation characteristics of current transformer are interfered by load impedance. To solve this problem, a dynamic impedance control strategy of voltage source converter is proposed. The method dynamically and continuously adjusts the secondary impedance according to the different states of the primary current, which not only meets the requirement of load power consumption under the condition of low current, but also reduces the influence of the magnetic saturation problem of the core. The principle of energy collection is analyzed, and the equivalent model of current transformer and circuit is established. The relationship between excitation characteristics and impedance parameters is discussed empirically-, which lays a foundation for the construction of the final control strategy. The experimental results show that, first, without capacitive impedance control, the effective value of ct induced voltage is 37.313V, and the corresponding energy collection power is 1.39W. Under the capacitive impedance control, the RMS value of ct induced voltage increases to 72.868V and the energy collection power reaches 5.31W. It can be seen that when the capacitive reactance state is changed, the RMS value of induced voltage and energy collection power are significantly different. Second, the output current of the strong current generator is adjusted to 70a, and when the current increases, the system enters the magnetic saturation state. At this time, the positive and negative half waves of the voltage and current on the CT secondary side produce high amplitude spike pulse waves, which is very dangerous to the insulation of the CT winding and the safety of the load equipment. Under the same conditions, after the induction impedance control is added to the CT core, the magnetic saturation state is relieved, and the waveform changes from peak wave to sine wave, the energy taking system returns to normal working state, the DC voltage output is normal, and the system power supply is stable. Third, the output current of the strong current generator continues to increase to 80a, with the increase of the primary current, the system waveform state remains stable, and the induced voltage and DC output voltage remain constant near the preset reference value. It can be concluded that the magnetic induction intensity of the core will be positively affected by the capacitive impedance operation, and the energy supply effect of the transformer will be increased. The operating characteristics of inductance impedance can reduce the magnetic induction intensity of the iron core, thus inhibiting the saturation of the iron core under high current operating conditions and maintaining stable power output. [ABSTRACT FROM AUTHOR]
- Published
- 2024
8. Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents.
- Author
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Wang, Aimin, Lin, Sheng, Wu, Guoxing, Li, Xiaopeng, and Wang, Tao
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STRAY currents , *DC transformers , *DISTRIBUTION (Probability theory) , *VIBRATION tests , *WEIGHING instruments , *ELECTRIC transformers - Abstract
Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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9. Transformer Discharge Carbon-Trace Detection Based on Improved MSRCR Image-Enhancement Algorithm and YOLOv8 Model.
- Author
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Ji, Hongxin, Han, Peilin, Li, Jiaqi, Liu, Xinghua, and Liu, Liqing
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ALGORITHMS , *ELECTRIC transformers , *REQUIREMENTS engineering , *IMAGE intensifiers , *POWER transformers , *FEATURE extraction , *PROBLEM solving - Abstract
It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools †.
- Author
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Hou, Chunming and Zheng, Liaomo
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REMAINING useful life , *TRANSFORMER models , *ELECTRIC transformers - Abstract
Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. Optimization of target detection model based on Yolov5 network with small samples.
- Author
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Sun, Hua, Su, Kaifeng, and Yang, Yifan
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SHOCK absorbers , *ELECTRIC transformers - Abstract
As an important part of automotive shock absorber, the columnar parts in automotive shock absorber will inevitably have machining defects during the process, which will not only degrade the performance of the parts, but also degrade or even fail the performance of the final shock absorber after assembly. Yolov5, as a target detection algorithm, has received much attention due to its high accuracy and fast operation speed. However, the algorithm faces some challenges when applied in a practical industrial environment. In this paper, improvement measures are proposed to address the limitations of sample collection and the high speed of pipeline recognition in industrial environments. The network model is optimized and designed. Firstly, the ASPP module is replaced by the SPP module thus improving the viewability throughout the process providing recognition accuracy. Secondly, the Conv and C3 layers of Yolov5s are replaced by Transformer to obtain higher recognition accuracy. By improving and optimizing the above methods, we can better cope with the improvement of detection accuracy under small sample conditions. Experiments show that the method can significantly improve the detection accuracy and operation speed of Yolov5s under the hardware condition of lower computing power, which is more suitable for industrial scenario application scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
12. Transient frequency response test and measurement error prediction of DCTV based on adaptive inertial weight improved ACO.
- Author
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Yang, Yutao, Zhai, Shaolei, Tang, Hansong, Duan, Genyue, and Deng, Liwu
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FREQUENCY response ,MEASUREMENT errors ,ELECTRIC transformers ,ARTIFICIAL intelligence ,DIRECT currents ,ANT algorithms - Abstract
A temporary frequency response test and measurement error prediction method of direct current voltage transformer (DCTV) based on artificial intelligence (AI) is proposed. Firstly, the frequency characteristic of direct current (DC) side voltage of DCTV is analyzed. On this basis, a DCTV transient Frequency Response testing method based on transient alternating current (AC) & DC superposition was developed. Then, the method of voltage sudden change and phase correction is used to achieve transient process DCTV response time testing. Finally, the ant colony optimization (ACO) algorithm was improved by combining an adaptive inertia weight improvement strategy, achieving accurate prediction of the Measurement Error of DCTV. The proposed AI based DCTV transient Frequency Response testing and Measurement Error prediction method were compared and analyzed with the other three methods through simulation experiments. Compared to the other three comparison methods, the maximum transformation error in the evaluation indicators of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) decreased by 0.006, 0.0119, and 0.0085, respectively, while the maximum phase error decreased by 0.2794, 0.3004, and 0.2823, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
13. Transforming the power of load balancing: Mitigating transformer losses in electric power distribution systems.
- Author
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Utomo, Restu Mukti, Muslimin, Alham, Nur Rani, Kurniawan, Ade, Afandi, Muhammad Jurdun Nur, and Al-Afifi, Umar Faruq
- Subjects
- *
ELECTRIC loss in electric power systems , *ELECTRIC power distribution grids , *ELECTRIC transformers , *IMPACT loads , *CURRENT transformers (Instrument transformer) , *ELECTRICAL load - Abstract
This article examines the issue of load imbalance in electric power distribution systems, which leads to current flow in the neutral conductor of transformers. The study aims to investigate the impact of load imbalance on neutral current and losses in the transformer. The analysis reveals that a load imbalance of 24% during the day results in a large neutral current of 109 A and increased losses due to neutral current flowing to the ground (8.47%). The findings highlight the significance of load balancing in reducing losses and improving the efficiency of electric power distribution systems. The study provides insights for researchers and practitioners to develop strategies for load balancing to mitigate the effects of load imbalance on transformer losses. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Algorithm for creating sketches to form a 3D diagram of an educational simulator in the subject of fundamentals of power supply.
- Author
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Rakhmonov, Ikromjon, Shayumova, Zamira, Obidov, Kamol, and Paxratdinov, Asamatdin
- Subjects
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POWER resources , *VOLTAGE regulators , *INTERACTIVE learning , *ELECTRIC transformers , *EDUCATIONAL planning , *EDUCATIONAL technology - Abstract
This paper discusses the development of a 3D educational simulator for teaching power supply system fundamentals. The simulator's creation followed a meticulously planned algorithm, beginning with clear educational objectives. It involved 2D conceptual designs and detailed 3D modeling of components like transformers and voltage regulators using software such as AutoCAD, SolidWorks, or Blender. The simulator's interactivity is a significant feature, allowing users to adjust parameters like voltage and observe real-time effects, enhancing understanding. It integrates theory with practical visualization, providing annotations and explanations for each component, creating a comprehensive learning experience. User feedback guided testing and iterative improvements, resulting in a user-friendly, adaptable simulator compatible with various educational platforms. This project highlights the importance of interactive learning tools in technical education, addressing challenges in accurate yet user-friendly educational technology design. It emphasizes the need for continuous improvement to align with evolving technical knowledge. In summary, this paper demonstrates the potential of advanced 3D simulations in STEM education, offering insights into effective tool design and implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. Calculation of active power losses taking into account the nonlinear inductance of the voltage transformer.
- Author
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Begmatov, Shavkat, Ibadullayev, Mukhtorkhon, and Dusmukhamedova, Saidakhon
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ELECTRIC inductance , *POWER resources , *TELECOMMUNICATION systems , *ELECTRIC transformers , *INFORMATION storage & retrieval systems , *MOBILE communication systems , *POWER factor measurement , *REACTIVE power - Abstract
Effective functioning of secondary power supply sources provides reliable power supply of information and communication systems of automation, telemechanics and communication. Voltage transformers in structural schemes of secondary power supply sources perform the processes of conversion and transformation of electrical signals and create active power losses due to nonlinear inductance, which leads to a decrease in the power factor of these devices. The proposed calculation method allows to determine more accurately the active power losses taking into account the nonlinear inductance of voltage transformers and contributes to the improvement of energy performance of secondary power supply sources. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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16. Characteristics of drawing transformer devices in electrical supply system and study of current converters.
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Amirov, Sulton, Shoyimov, Yulchi, Matkasimova, Shaxnoza, Numonov, Abbos, Xolmurzayeva, Shaxnoza, and Ganiyev, Elbek
- Subjects
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ELECTRICAL supplies , *POWER resources , *CURRENT transformers (Instrument transformer) , *ELECTRIC transformers , *POWER transformers , *DC-to-DC converters - Abstract
In this article, it is determined that the current measuring transformers used in traction transformer power supply control systems should have extended measurement functionality, adjustable range, linear switching function, high sensitivity and accuracy, and stable characteristics even under severe operating conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
17. Application of fuzzy system to determine transformer losses at substations of PLN in the special region of Yogyakarta.
- Author
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Sukisno, Toto, Nurhayadi, Abadi, Agus Maman, and Kahfi, Aulia
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FUZZY systems , *ELECTRIC transformers , *ELECTRICAL energy , *DATA envelopment analysis , *PERCENTILES - Abstract
Transformer losses are one of the factors to be considered in the distribution of electrical energy. In the Special Region of Yogyakarta, there are 12 transformers with a capacity of 60 MVA to meet the electrical energy needs of PLN in Yogyakarta area. This study aims to determine the transformer losses of each substation transformer in the Special Region of Yogyakarta by using the Mamdani and zero order Sugeno fuzzy systems. To construct fuzzy systems, the data are taken from PLN in Special Region of Yogyakarta. The input variables of Mamdani model are current R, current S, current T, and transformer voltage. The output of Mamdani model is transformer loading percentage. Then, output of Mamdani model is used as input of zero order Sugeno model to get the efficiency value of transformer and then, the transformer losses are determined. The results are that loading percentage, efficiency value, and losses of each transformer have been determined. Furthermore, the results show that the model accuracy of transformer losses was 84.1% for training data and 82.74% for testing data. Based on all data, the greatest average of transformer losses occurs at transformer I Bantul, while transformer II Wates has the smallest average transformer losses. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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18. HOW RADAR WAS INVENTED: The scientific discoveries and mechanical milestones that led to the creation of one of the world's most revolutionary technologies
- Author
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Dutfield, Scott
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Electromagnetic waves ,Electric transformers ,Physicists ,Electromagnetic radiation ,Radar systems ,Electromagnetism ,Magnetic fields ,Electric waves ,Science and technology - Abstract
FINDING RADIO WAVES 1865 In the late 1800s, German physicist Heinrich Hertz made a discovery that would change the world forever. Before Hertz' discovery, Scottish physicist James Clerk Maxwell had [...]
- Published
- 2024
19. A 360-Degree Approach to Wildfires in Canada: FortisAlberta combines conventional and innovative approaches to manage wildfire risk in a diverse operating environment
- Author
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Eck, Curtis
- Subjects
Electric transformers ,Wildfires ,Company business management ,Electronics and electrical industries - Abstract
With the Rocky Mountains on its western border and prairies to the east, the Canadian province of Alberta boasts a wide variety of natural landscapes. An abundance of forested locations [...]
- Published
- 2024
20. Protect Wind Turbines from Lightning Strikes: Wind turbine owners and operators should ensure their lightning protection system has been installed correctly and regularly check it is working properly
- Author
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Kulkarni, Sameer and El-Rasheed, Ahmed
- Subjects
Nuclear energy ,Turbine industry ,Air-turbines ,Electric transformers ,Wind power ,Green technology ,Electric power production ,Electronics and electrical industries - Abstract
The share of wind power in total electric power generation is expected to increase, and with that comes a requirement for this carbon-free source to be more reliable. The most [...]
- Published
- 2024
21. Single-Stage MV-Connected Charger Using an Ac/Ac Modular Multilevel Converter.
- Author
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Marca, Ygor Pereira, Roes, Maurice G. L., Wijnands, Cornelis G. E., Duarte, Jorge L., and Huisman, Henk
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AC DC transformers , *BATTERY chargers , *ELECTRIC vehicle batteries , *SQUARE waves , *ELECTRIC transformers , *ELECTRIC vehicles , *POWER density , *HYBRID electric vehicles - Abstract
Modular multilevel converters with non-sinusoidal ac voltage output can reduce cost and volume in medium-voltage-connected electric vehicle battery charging applications. The use of full-bridge submodules in such converters enables single-stage ac/ac voltage conversion, allowing a medium-voltage grid to be directly connected to a medium-frequency isolation transformer. The application of a square wave voltage at the medium-frequency transformer's single-phase port enhances the converter's efficiency and power density in comparison to a sinusoidal voltage. This paper presents the analysis and modelling of a modular multilevel converter, comparing its operation with sinusoidal and square wave output voltages. A single control scheme for both output voltage waveforms is proposed for the three-phase and single-phase ac currents, circulating currents, and the energy stored in the submodule capacitors. The control strategy of the three-phase and single-phase port currents is verified through simulation and experiments using a scaled-down prototype, thereby validating its suitability for high-power bidirectional battery chargers. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
22. Bidirectional transformer with knowledge graph for video captioning.
- Author
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Zhong, Maosheng, Chen, Youde, Zhang, Hao, Xiong, Hao, and Wang, Zhixiang
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KNOWLEDGE graphs ,TRANSFORMER models ,VIDEOS ,ELECTRIC transformers - Abstract
Models based on transformer architecture have risen to prominence for video captioning. However, most models are only to improve either the encoder or the decoder, because when we improve the encoder and decoder simultaneously, the shortcomings of either side may be amplified. Based on the transformer architecture, we connect a bidirectional decoder and an encoder that integrates fine-grained spatio-temporal features, objects, and relationships between the objects in the video. Experiments show that improvements in the encoder amplify the information leakage of the bidirectional decoder and further produce a worse result. To tackle this problem, we generate pseudo reverse captions and propose a Bidirectional Transformer with Knowledge Graph (BTKG), which integrates the outputs of two encoders into the forward and backward decoders of the bidirectional decoder, respectively. In addition, we make fine-grained improvements on the interior of the different encoders according to four modal features of the video. Experiments on two mainstream benchmark datasets, i.e., MSVD and MSR-VTT, demonstrate the effectiveness of BTKG, which achieves state-of-the-art performance in significant metrics. Moreover, the sentences generated by BTKG contain scene words and modifiers, that are more in line with human language habits. Codes are available on https://github.com/nickchen121/BTKG. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Power Transformer On-Load Capacity-Regulating Control and Optimization Based on Load Forecasting and Hesitant Fuzzy Control.
- Author
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Zou, Dexu, Sun, Xinyu, Quan, Hao, Yin, Jianhua, Peng, Qingjun, Wang, Shan, Dai, Weiju, and Hong, Zhihu
- Subjects
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POWER transformers , *DUNG beetles , *ELECTRIC transformers , *FORECASTING , *OPERATING costs , *SECURITY systems - Abstract
The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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24. Theoretical and practical analysis of parallel operating conditions of two transformers with different relative short circuit voltages.
- Author
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KUCUKKAYA, Burak Bugra and PAMUK, Nihat
- Subjects
- *
POWER resources , *SHORT circuits , *ELECTRIC power distribution , *POWER transformers , *ELECTRICAL energy , *VOLTAGE , *ELECTRIC transformers - Abstract
The safety of the electrical energy supply is one of the most critical issues for companies engaged in production. The internal distribution network, which will provide the energy for the production elements, is essential. Power Transformers, which energize the loads fed from the electricity distribution network, are backed up in a way suitable for parallel operation for energy supply security. As a result of this situation, in case of a predictive failure in one of the transformers, energy supply safety is ensured since the other transformer is designed to carry the load alone. When one of these identical transformers is damaged, the relative short-circuit voltage of the new transformer to be integrated into the system may be different. The transformers with different short-circuit voltages are not shared their load equally. For this reason, the total amount of load that they can be loaded, the relative short-circuit voltage that will occur, and the load rates they are loaded change. In this study, a detailed analysis of two transformers with different relative short-circuit voltages and operating in parallel was made, both theoretically and as a result of the application, with the information obtained through the SCADA program. The theoretical values were calculated as a result of the analysis, the SCADA values were compared, and the margin of safety between them was established. It has been shown that the safety margin will protect against overload on a low short-circuit transformer. After the commissioning, the load rating needs to check and it can be regulated in line with the load rates that will occur after commissioning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
25. Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques.
- Author
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Wang, Zeyi, Liu, Guangzhi, Pang, Dan, Wang, Yao, Yu, Bin, Wang, Zhenhao, Xiao, Huangqing, and Shi, Linjun
- Subjects
POWER resources ,MAXIMUM power point trackers ,MATHEMATICAL optimization ,ELECTRIC transformers - Abstract
With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Fault Tolerant Dual Active Bridge Converter for Electric Vehicle Application.
- Author
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Babalou, Milad, Torkaman, Hossein, Pouresmaeil, Edris, and Pourmoradi, Nazanin
- Subjects
ELECTRIC vehicles ,FAULT tolerance (Engineering) ,ELECTRIC transformers ,ELECTRIC inductors ,METAL oxide semiconductor field-effect transistors - Abstract
In this paper, a dual-active bridge converter based on the utilization of two transformers is presented. The principles of operation, switching strategy, and transmission power characteristics of the proposed converter under normal operation are discussed, comprehensively. Moreover, the RMS current of two transformers with different values of inductances of the inductors that are in series with the transformers; is discussed. The operation of the proposed dual active bridge (DAB) converter under the open-circuit failure of transformers is studied. In addition, the loss distribution of the proposed converter in different powers is investigated. The proposed dual-transformer-based dual-active bridge converter is compared with the presented converters. Finally, the proposed converter with a low-voltage side (VL= 300 V), the switching frequency of power MOSFETs (fs= 50 kHz), and an accurate model of the electric battery at a high-voltage side (VH= 450 V) are simulated to verify the way of charging and discharging the electrical battery with the proposed converter under normal and open-circuit fault of transformers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
27. Dependencies of Parameter and Load Toque Sensitivities of Electric Motor Outputs on Design Requirements.
- Author
-
Sevinç, Ata
- Subjects
ELECTRIC motors ,TORQUE ,ELECTRIC transformers ,SENSITIVITY analysis ,PARAMETER estimation - Abstract
Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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28. Early fire detection technology based on improved transformers in aircraft cargo compartments.
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Hong-zhou Ai, Dong Han, Xin-zhi Wang, Quan-yi Liu, Yue Wang, Meng-yue Li, and Pei Zhu
- Subjects
FIRE detectors ,ELECTRIC transformers ,DEEP learning ,RECURRENT neural networks ,ARTIFICIAL neural networks - Abstract
The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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29. A Predictive Model for Voltage Transformer Ratio Error Considering Load Variations.
- Author
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Li, Zhenhua, Cui, Jiuxi, Rocha, Paulo R. F., Abu-Siada, Ahmed, Li, Hongbin, and Qiu, Li
- Subjects
ELECTRIC transformers ,ELECTRIC charge ,TRANSFORMER models ,DEEP learning ,PREDICTION models ,RANK correlation (Statistics) ,ERRORS-in-variables models - Abstract
The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method's robustness and superiority in accurately forecasting VT measurement error trends. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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30. EVALUATION OF THERMAL CONDUCTIVITY USING NANOFLUIDS TO IMPROVE THE COOLING OF HIGH VOLTAGE TRANSFORMERS.
- Author
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BOURAS, AbdelKrim, TALOUB, Djedid, CHAMKHA, Ali J., and DRISS, Zied
- Subjects
- *
ELECTRIC transformers , *NANOFLUIDS , *RAYLEIGH number , *HIGH voltages , *HEAT transfer , *THERMAL conductivity , *COOLING , *FREE convection - Abstract
This paper was written to demonstrate the value of using nanofluids for cooling high power transformers while also providing current techniques for business and academia. A numerical analysis of the improvement caused by the cooling of a high voltage transformer using nanofluids has been done. A tank with a temperature source inside and a charge of mineral oil-barium titanate nanoparticles is used to study natural-convection. This study investigates the effects of variables on the thermal efficiency of the tank, including the thermal Rayleigh number and volume fraction. The results show that quenching varies with low and high Rayleigh thermal numbers and depends on the volume percentage of used nanoparticles. The effects were illustrated in thermal transfer rate representations as functions of the thermal Rayleigh number (Rat = 103 and 106) and the solid volume particle from the nanoparticles (0% = f < 10%). The findings showed that improving the solid volume particle of the nanoparticles by 10% causes the fluid being utilized to become more effectively conductive, which improves the rate of heat transfer by roughly 10% when compared to the case of the base fluid. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Development and Validation of the High-Voltage Direct-Current Modular Multilevel Converter (HVDC-MMC) Model for Converter Transformer Protection Studies.
- Author
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Solak, Krzysztof, Rebizant, Waldemar, and Mieske, Frank
- Subjects
- *
PHASE-locked loops , *CURRENT transformers (Instrument transformer) , *FAULT currents , *ELECTRIC transformers , *VECTOR control - Abstract
The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer.
- Author
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Long, Zhecheng, Fan, Shidong, Gao, Qian, Wei, Wei, and Jiang, Pan
- Subjects
CONVOLUTIONAL neural networks ,TRANSFORMER models ,DREDGES ,ELECTRIC transformers ,PRESSURE sensors ,WATER pressure ,MUD ,BIG data - Abstract
The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger's (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger "Hua An Long" was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. A lightweight siamese transformer for few-shot semantic segmentation.
- Author
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Zhu, Hegui, Zhou, Yange, Jiang, Cong, Yang, Lianping, Jiang, Wuming, and Wang, Zhimu
- Subjects
- *
TRANSFORMER models , *PROTOTYPES , *ELECTRIC transformers , *POWER transformers - Abstract
Few-shot semantic segmentation (FSS) is a challenging task that aims to segment new classes in query images with a few annotated support samples. One inherent challenge in FSS is the intra-class variation resulting from the limited availability of support samples and the diversity of query data. Current methods frequently employ prototype-support techniques to tackle this issue. However, it is important to note that a single support prototype shares limited commonalities with query features, and increases the difficulty of accurate segmentation. In this paper, we propose a lightweight and effective framework named Siamese Transformer (SiaT) with a mere 0.68M learnable parameters to enhance commonalities between prototypes and query features. The SiaT framework consists of two key modules: the Siamese Transformer Module (STM) and the Query Activation Module (QAM). The STM integrates two shared Transformer decoders for information propagation to generate two enhanced prototypes. One Transformer decoder facilitates the propagation of target-related information from the query to the original support prototype, while the other propagates foreground information from the support to the initial query prototype. The QAM utilizes the two enhanced prototypes from the STM as support information, engaging with query features through channel-wise allocation and concatenation, termed query activation. Moreover, SiaT showcases competitive performance on the widely used benchmarks PASCAL- 5 i and COCO- 20 i , which demonstrates its effectiveness in addressing the intra-class variation challenge within FSS tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Sentiment Analysis Combining Dynamic Gradient and Multi-view Co-attention.
- Author
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WANG Xiang, MAO Li, CHEN Qidong, and SUN Jun
- Subjects
SENTIMENT analysis ,LONG-term memory ,ELECTRIC transformers - Abstract
Aiming at the problems of unbalanced inter-modal optimization and inadequate fusion of multimodal features in multimodal sentiment analysis, a multimodal sentiment analysis model combining dynamic gradient mechanism and multi-view co-attention mechanism (DG-MCM) is proposed, which can effectively mine single-modal representation and fully integrate multimodal information. Firstly, the model uses pre-trained model BERT (bidirectional encoder representation from transformers) and stacked long short- term memory (SLSTM) to learn the features of text, audio and video, and proposes a dynamic gradient mechanism. By monitoring the contribution difference and learning speed of each mode, the feature learning of each mode is assisted. Secondly, the features of different modes obtained are fused using the multi-view co-attention mechanism. By projecting every two modes into multiple spaces for interaction, more adequate fusion features are obtained. Finally, fusion features and single-modal features are spliced together for sentiment prediction. Experimental results on CMU-MOSI and CMU-MOSEI datasets show that this model can fully learn information between single mode and different modes, and effectively improve the ac- curacy of multimodal sentiment analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Towards 500 kV power transformers damaged in the 2022 Luding earthquake: field investigation, failure analysis and seismic retrofitting.
- Author
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Zhu, Wang, Xie, Qiang, Liu, Xiao, Mao, Baojun, and Xue, Zhihang
- Subjects
FAILURE analysis ,FIELD research ,RETROFITTING ,EARTHQUAKES ,ELECTRIC power distribution grids ,ELECTRIC transformers ,POWER transformers - Abstract
The Ms 6.8 Luding earthquake struck Sichuan province in China on September 5, 2022, triggering many failures of electrical installations in power grids. All high-voltage bushings of transformers in a 500 kV substation were damaged during the earthquake. Subsequent field investigation of the transformers was carried out, and a simulation model was developed to study the failure mechanism using the acceleration time histories recorded by a seismograph station near the substation. An equivalent modeling technique in the simulation model for the rotational stiffness of the flange joint per relevant code was proposed. Both the modal analysis and dynamic amplification analysis were then performed. After that, two types of improved flange configurations were suggested for newly designed and existing transformer bushings, respectively. Their effectiveness on stress optimization was validated. The results reveal that the first eigenfrequency of the transformer is close to the highest spectral peak of the recorded acceleration time histories, indicating obvious amplification effects. The stress distributions of the flange of the simulation model well match with the engineering failure, and the hitch lugs of the connection flanges are the leading cause that results in the cracks. Adopting the flange with stiffeners and adding axial curved shells and new hitch lugs between the flange plates can enhance the bending stiffness of the HV bushing and dramatically reduce the stresses of the flange at the positions where the engineering cracks propagated. Moreover, some instructive suggestions were proposed for assisting the seismic design and retrofitting of transformers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. ELECTRIC ENERGY METERING ERROR EVALUATION METHOD BASED ON DEEP LEARNING.
- Author
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TIANFU HUANG, ZHIWU WU, WEN ZHAN, CHUNGUANG WANG, and TONGYAO LIN
- Subjects
ELECTRIC meters ,ELECTRICITY power meters ,CURRENT transformers (Instrument transformer) ,ELECTRIC transformers ,DEEP learning ,EVALUATION methodology - Abstract
The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Cinematographic Camera Diffusion Model.
- Author
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Jiang, Hongda, Wang, Xi, Christie, Marc, Liu, Libin, and Chen, Baoquan
- Subjects
CAMERAS ,TRANSFORMER models ,VIRTUAL reality ,INFRARED cameras ,ELECTRIC transformers ,ARTIFICIAL intelligence - Abstract
Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Analysis of the Influence of Insulation Moisture during Long-Term Exploitation of Transformers on Their Reliability as Determined by Alternating Current Electrical Parameters.
- Author
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Zukowski, Pawel, Kierczynski, Konrad, Rogalski, Przemyslaw, Bondariev, Vitalii, Zenker, Marek, Pajak, Rafal, Szrot, Marek, Molenda, Pawel, and Koltunowicz, Tomasz N.
- Subjects
- *
ELECTRIC transformers , *POWER transformers , *DIELECTRIC relaxation , *ELECTRON tunneling , *MOISTURE , *TRANSFORMER insulation , *PERMEABILITY , *ALTERNATING currents - Abstract
This paper presents the results of the study of the direct current (DC) and alternating current (AC) electrical properties of an electrical pressboard–bio-insulating oil–water composite in a wide range of water content and temperatures used in electric power transformers. These parameters allow the level of insulation reliability to be determined after many years of operation of power transformers. To analyse the experimental results, a model of the DC and AC conductivities of nanocomposites based on the quantum-mechanical phenomenon of electron tunnelling was used. It was found that in a low-frequency region, the conductivities of AC and DC and their activation energy are equal. The relaxation times of AC conductivity and permittivity are also equal. It was found that the dependence of the DC conductivity on the distance between water molecules is an exponential function. On the basis of the model of conductivity by electron tunnelling between potential wells, the average number of water molecules in a nanodroplet, located in a composite of electrical pressboard–bio-insulating oil–moisture was determined to be (126 ± 20). It was found that the measured dependencies of DC and AC conductivity, permeability and dielectric relaxation times are consistent with the results of computer simulations performed on the basis of the model. This study showed that the composite of pressboard impregnated with bio-oil spontaneously transforms through water absorption into a pressboard–bio-oil–water nanocomposite. These will serve as the basis for the application of actual conductivity and dielectric relaxation mechanisms to improve the accuracy of moisture estimation in the solid component of power transformer insulation carried out on the basis of measurements of DC and AC properties. This will improve the operational safety of the transformers, minimise the occurrence of transformer failure and the associated environmental pollution. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Trends in Measuring Instrument Transformers for Gas-Insulated Switchgears: A Review.
- Author
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Kim, Dong-Eon, Lee, Gyeong-Yeol, Kil, Gyung-Suk, and Kim, Sung-Wook
- Subjects
- *
MEASURING instruments , *ELECTRIC transformers , *CURRENT transformers (Instrument transformer) , *DIGITAL communications , *PASSIVE components - Abstract
Voltage and current measurements in high-voltage substations are fundamental for stable operation. Conventional instrument transformers (ITs) face challenges in gas-insulated switchgears (GISs), such as size, weight, accuracy limitations, and behavioral instability at abnormal voltages and currents. Non-conventional instrument transformers (NCITs) have emerged to address these issues, complying with International Electrotechnical Commission (IEC) standards and providing millivolt-level signals, enabling downsizing of GIS bays. The transition to digital substations, as mandated by IEC 61850-9-2, requires a shift from the conventional 110 V/5 A outputs to levels ranging from millivolts to volts. Electronic instrument transformers (EITs), compliant with the IEC 60044-7 and 8 standards, offer alternatives to conventional ITs with smaller sizes and wider frequency ranges. However, issues remain with EITs, including limited adoption, the necessity of separate power sources, and susceptibility to electromagnetic interference. Recent standards, transitioning to IEC 61869, focus on low-power instrument transformers (LPITs). Low-power voltage transformers (LPVTs) and low-power current transformers (LPCTs), designed with passive components, present potential solutions by directly connecting to merging units (MUs) for digital signal transmission. This review outlines the current status of various IT standards, covering conventional ITs, EITs based on IEC 60044-7 and 8, and LPITs based on IEC 61869-10 and 11. Advancements in sensor technology relevant to these standards are also explored. The paper provides insights into the evolving landscape of instrument transformers, addressing challenges and offering potential pathways for future developments in digital substations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Surgical Phase Recognition in Laparoscopic Cholecystectomy.
- Author
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Li, Yunfan, Gupta, Himanshu, Prasanna, Prateek, Ramakrishnan, IV, and Ling, Haibin
- Subjects
CHOLECYSTECTOMY ,COMPUTER vision ,LAPAROSCOPIC surgery ,OPERATIVE surgery ,SURGICAL robots ,ELECTRIC transformers - Abstract
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. Previous works on automated recognition of surgical phases utilized popular methods in computer vision but failed to consider the sequential nature of surgical procedures. In this paper, we propose a method that utilizes calibrated confidence scores to dynamically switch between two Transformer-based models, viz., baseline and a separately trained binary classifier model, depending on the calibrated confidence level. Our method outperforms the baseline model on the publicly available Cholec80 dataset and can be readily applied to a variety of phase recognition methods and applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. Learning spatiotemporal relationships with a unified framework for video object segmentation.
- Author
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Mei, Jianbiao, Wang, Mengmeng, Yang, Yu, Li, Zizhang, and Liu, Yong
- Subjects
TRANSFORMER models ,ELECTRIC transformers ,VIDEOS - Abstract
Video object segmentation (VOS) has made significant progress with matching-based methods, but most approaches still show two problems. Firstly, they apply a complicated and redundant two-extractor pipeline to use more reference frames for cues, increasing the models' parameters and complexity. Secondly, most of these methods neglect the spatial relationships (inside each frame) and do not fully model the temporal relationships (among different frames), i.e., they need adequate modeling of spatial-temporal relationships. In this paper, to address the two problems, we propose a unified transformer-based framework for VOS, a compact and unified single-extractor pipeline with strong spatial and temporal interaction ability. Specifically, to slim the common-used two-extractor pipeline while keeping the model's effectiveness and flexibility, we design a single dynamic feature extractor with an ingenious dynamic input adapter to encode two significant inputs, i.e., reference sets (historical frames with predicted masks) and query frame (current frame), respectively. Moreover, the relationships among different frames and inside every frame are crucial for this task. We introduce a vision transformer to exploit and model both the temporal and spatial relationships simultaneously. By the cascaded design of the proposed dynamic feature extractor, transformer-based relationship module, and target-enhanced segmentation, our model implements a unified and compact pipeline for VOS. Extensive experiments demonstrate the superiority of our model over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. We also explore potential solutions, such as sequence organizers, to improve the model's efficiency. On DAVIS17 validation, we achieve ∼ 50% faster inference speed with only a slight 0.2% ( J & F ) drop in segmentation quality. Codes are available at https://github.com/sallymmx/TransVOS.git. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Choosing Optimal Supply Radius of Transformer Substations (TSs) in Iraq's Cities Using Geometric Programming with Neutrosophic Coefficients.
- Author
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Essa, Ahmed K., Mitungwi, Montifort Blessings Andrew, Gadama, Tuweh Prince, and Salama, Ahmed A.
- Subjects
- *
NEUTROSOPHIC logic , *FUZZY logic , *POWER resources , *ELECTRIC transformers , *ELECTRIC substations - Abstract
Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, then y is very likely to occur," "x should not be less than a," etc. To design an optimal radius of power supply in the electrical transformer substation, this article presents a new method for creating primal and dual neutrosophic geometric programming problems. It also provides a numerical example to evaluate the approximate optimal economic power supply radius. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Performance comparison of visual transformer based models for shoulder implant classification.
- Author
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Kablan, Elif Baykal and Kablan, Yavuz
- Subjects
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ELECTRIC transformers , *ARTHROPLASTY , *X-ray imaging , *PROSTHETICS , *SHOULDER joint - Abstract
Total shoulder arthroplasty (TSA) is a surgical procedure addressing severe pain and restricted shoulder joint movement. During TSA surgery, X-ray images guide the selection of the prosthetic implant suitable for the patient from a variety of models produced by different manufacturers. However, prostheses may wear or loosen over time, thus requiring periodic evaluation and replacement. Currently, the process involves taking new X- ray images from patients, resulting in variability in expert opinions on implant types. Therefore, there is a need for highly accurate automated diagnostic systems to help recognize unknown implants. In this study, we present a performance comparison of vision transformer (ViT) based models for automatic shoulder implant classification from X-ray images. Fine-tuning of pre-trained ViT models on a publicly available shoulder X-ray dataset showed high success in terms of accuracy, precision, sensitivity, and F-measure metrics. The Swin-B model yielded the highest results with 93.84\% accuracy, 88.15\% precision, and 85.52\% recall. These results showed that ViT based models can help improve treatment planning by providing reliable identification of shoulder implant manufacturers and model information and time efficiency, especially for specialists. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
44. Microsecond fast, battery powered, 100 kV modular pulse charger utilizing pulse transformers.
- Author
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Klein, Tyler, Eulenbach, Andrew, Neuber, Andreas, and Dickens, James
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ELECTRIC batteries , *ELECTRIC transformers , *INFORMATION resources management , *NUMBER systems , *CAPACITORS , *MICROCONTROLLERS - Abstract
Multiple pulse charging modules were designed and tested for synchronous use in scaled experiments. The pulse chargers are each capable of charging a nanofarad-sized capacitive load to 100 kV in less than 10 µs. This is achieved by initially charging a microfarad-sized capacitor to a maximum of 3 kV, then, using a thyristor switch, discharging the capacitor into two transformers paralleled on the primary side. Each transformer steps the voltage up to 50 kV, with the transformer having opposing polarities and a common neutral on the secondary. Thus, the full 100 kV is obtained by placing the nanofarad-sized capacitor between the +50 and −50 kV terminals. The pulse chargers use a microcontroller to monitor and control the pulse charger in addition to communicating with the user as well as other pulse chargers. For increased battery life, each pulse charger is kept in a low-power state while not in use. Upon startup, the pulse chargers automatically detect the number of modules in the system, the position of itself in the system, and the timing delays associated with each pulse charger in the system. This information is essential for synchronous charging and provides individual information and controls for the pulse chargers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features.
- Author
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Dabaghi-Zarandi, Fataneh, Behjat, Vahid, Gauvin, Michel, Picher, Patrick, Ezzaidi, Hassan, and Fofana, Issouf
- Subjects
- *
DEEP learning , *PROBABILITY density function , *POWER transformers , *DATA acquisition systems , *ELECTRIC transformers - Abstract
An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC's condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC's condition. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Innovative Matrix-Type AC-AC Solid-State Transformer Eliminating Ripple Power with Double-Grid Frequency.
- Author
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Wang, Hui, Yu, Tianshi, Wang, Manlin, and Ning, Guangfu
- Subjects
- *
ENERGY storage , *ELECTRIC transformers , *AC DC transformers , *ZERO voltage switching , *POWER density - Abstract
Single-phase solid-state transformers (SSTs) have the advantages of a compact structure, higher reliability, and multiple functions, and have been widely studied. However, bulky energy storage elements and inherent ripple power with double-grid frequency issues are the main disadvantages of conventional single-phase SSTs. This paper presents a single-phase matrix-type AC-AC SST eliminating ripple power with double-grid frequency. The presented SST consists of a line-frequency-commutated rectifier without bulky DC-link capacitors, an LLC resonant converter, a buck converter, and a line-frequency-commutated inverter. The LLC operates efficiently with a fixed voltage gain, and the buck converter provides a voltage regulation function. As a result, high conversion efficiency, high power density, and potentially high reliability are achieved. A 1 kW SST prototype is developed and tested to validate the feasibility and functionality of the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. A Fast and Accurate Lane Detection Method Based on Row Anchor and Transformer Structure.
- Author
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Chai, Yuxuan, Wang, Shixian, and Zhang, Zhijia
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TRANSFORMER models , *DRIVER assistance systems , *ROAD markings , *ELECTRIC transformers , *POWER transformers - Abstract
Lane detection plays a pivotal role in the successful implementation of Advanced Driver Assistance Systems (ADASs), which are essential for detecting the road's lane markings and determining the vehicle's position, thereby influencing subsequent decision making. However, current deep learning-based lane detection methods encounter challenges. Firstly, the on-board hardware limitations necessitate an exceptionally fast prediction speed for the lane detection method. Secondly, improvements are required for effective lane detection in complex scenarios. This paper addresses these issues by enhancing the row-anchor-based lane detection method. The Transformer encoder–decoder structure is leveraged as the row classification enhances the model's capability to extract global features and detect lane lines in intricate environments. The Feature-aligned Pyramid Network (FaPN) structure serves as an auxiliary branch, complemented by a novel structural loss with expectation loss, further refining the method's accuracy. The experimental results demonstrate our method's commendable accuracy and real-time performance, achieving a rapid prediction speed of 129 FPS (the single prediction time of the model on RTX3080 is 15.72 ms) and a 96.16% accuracy on the Tusimple dataset—a 3.32% improvement compared to the baseline method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Double B-type magnetic integrated transformer based input-parallel output-parallel LLC resonant converter modules.
- Author
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Xia, Nenghong, Yan, Shuang, Ma, Huaqi, Mao, Xike, Chen, Mengqi, Duan, Bin, and Acuña,, Edgar Marcial Maqueda
- Subjects
MAGNETIC flux density ,EDDY current losses ,POWER density ,MAGNETIC fields ,ELECTROMAGNETIC interference ,ELECTRIC transformers ,POWER transformers - Abstract
The LLC resonant converter used in high-power situations suffers from the problems of high conduction loss and current stress, which can be solved using input-parallel output-parallel (IPOP)-connected converter modules. However, this leads to a multiple increase in the number of magnetic components, which reduces power density. Magnetic integration technology is an effective way to reduce the volume of converters. Currently, the magnetic integrated transformer based on EE-type cores is widely used to realize miniaturization, and it uses leakage inductance instead of resonant inductance to improve power density. However, leakage inductance is difficult to control, and the external radiated magnetic field will produce serious eddy current loss and electromagnetic interference. This article proposes a novel double B-type magnetic integrated transformer, which can integrate the magnetic components of two LLC resonant converters simultaneously and where the resonant inductances are wound independently. The structure contains four low reluctance branches, which are used as the cores of the transformer and the resonant inductance. The decoupling integration method, which integrates the four components into a single core, has been used to increase core utilization and improve power density. On this basis, the transformer's high- and low-voltage windings are cross-arranged to reduce the magnetic field intensity in space, further decreasing the loss and electromagnetic interference. Compared with the EE-type magnetic integrated transformer, the volume of the proposed structure is reduced by 5.9%. A 400W experimental prototype is built, and the results verify the validity of the design. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Study on the Preparation and Aging Performance of Temperature-Indicating Patch Used for Thermal Defect Detection of Transformer Bushing Cylinder Head.
- Author
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Wang, Mingqin, Wu, Xiaobin, Jiang, Nan, Liu, Chenghua, Xian, Richang, and Jia, Wentao
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BUSHINGS ,CONTACT angle ,HIGH temperatures ,ELECTRIC transformers ,COLOR temperature ,MEASURING instruments - Abstract
Elevated temperatures at the transformer bushing cylinder head can precipitate failures, leading to significant power outages. In response, this study introduces a reversible temperature-indicating patch for the nuanced detection of thermal anomalies in the transformer bushing's cylinder head. The patch, crafted through a melting process, utilizes a reversible discoloration material and is developed in two variants via an adsorption substrate method. Comprehensive evaluations of the patches' color-changing characteristics, alongside their electrical and hydrophobic properties, were conducted using an automatic contact angle measuring instrument and an AC flashover test platform. The findings reveal that the temperature-indicating patch exhibits a discernible color transition within the range of 49~55 °C, with a color reversion temperature span of 45~55 °C, denoting marked sensitivity and robust reversibility. Additionally, it was observed that prolonged thermal aging correlates with a decrease in both the water contact angle and the discharge voltage per unit length across the surface of the patches, indicating a degradation in performance. Among the variants, the binder-based temperature-indicating patch demonstrated superior stability in electrical performance compared to its vacuum-based counterpart. The outcomes of this research offer valuable insights for the development of advanced diagnostic tools for the identification of thermal defects in transformer bushings, potentially enhancing reliability and safety in power distribution systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Agile fusion: developing 'Eat at Right Place' sentiment analysis tool.
- Author
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Prabhune, Akash, Srihari, Vinay R., Sethiya, Neeraj Kumar, and Gauniyal, Mansi
- Subjects
SENTIMENT analysis ,SPRINTING ,SCRUM (Computer software development) ,ELECTRIC transformers - Abstract
This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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