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The parameter of tensile strength for transformer bushing insulating paper occupies an important position in the structural design and life evaluation of transformer bushing. In order to precisely achieve the effect of aging time and moisture content on the tensile strength of transformer bushing paper, transformer insulating paper samples with a thickness of 0.13 mm were prepared for tensile strength and dielectric strength testing under different moisture contents and aging states. The results show that the tensile strength (MPa) of insulating paper decreases linearly in the range of 0–648 h under accelerated thermal aging at 130 °C. However, there is a more serious exponential decline in the tensile strength (MPa) of insulation paper with its moisture content is boosted. The moisture content in insulation paper is the most important factor for insulation paper deterioration in oil-impregnated paper insulation equipment, which could accelerate the end of insulation service life. The value of capacitance increment ΔC of the insulation paper samples displayed closely linear growth with a moisture content from 0.5% to 5.9%. Strictly controlling the moisture content by monitoring the ΔC value of transformer insulation paper is of significance for prolonging its service life. [ABSTRACT FROM AUTHOR]

The stable operation of transformers is an important guarantee of power system security. The temperature distribution of main insulation is different in long‐term operation. The temperature difference between the local hottest spot temperature and the average oil temperature in the transformer exceeds 30 K. Therefore, the oil‐paper insulation inside the transformer will exhibit non‐uniform ageing after long‐term operation. At present, frequency domain dielectric response (FDS) technology is widely used in transformer ageing assessment. However, the existing assessment methods are only used for overall insulation ageing assessment. The average moisture content and polymerization degree of the overall insulation between each phase winding are obtained. But it still cannot be used to assess the non‐uniform ageing of the transformer's main insulation. The radial, axial, and two directions non‐uniform ageing models have been made in the laboratory in this study. The FDS and physicochemical properties of non‐uniform ageing models were tested. The variation law of FDS for non‐uniform ageing models was analysed. Meanwhile, the dielectric relaxation model of non‐uniform ageing for main insulation was established. The method of frequency division iterative calculation was proposed. Combined with the Fessler moisture balance formula of ageing oil‐paper insulation, the quantitative assessment method for ageing parameters of the non‐uniform ageing model can be realized. Compared the calculated results with the chemical analysis results, the relative error of the polymerization degree calculation is less than 4%, and the error of the moisture content calculation is less than 0.3%. The calculation results can meet the needs of the engineering apply. Therefore, this method can realize the quantitative assessment of transformer main insulation ageing, which is of great significance to assess the transformer insulation state. [ABSTRACT FROM AUTHOR]

• The influence of nanoparticles on the dielectric properties of neem oil was investigated. • Dielectric properties of the base liquid increases with nanoparticles loading. • Effect of temperature on the dielectric loss of the nanofluids was investigated. • The compatibility test of the nanofluid with kraft paper was investigated. • Nanofluid has no pronounce effects on the dielectric properties of kraft paper. Methyl ester from vegetable oil has been considered a potential replacement for mineral oil used for power transformer cooling and insulating applications. However, some pertinent properties in making this liquid a better transformer oil based on the laydown standards need to be enhanced to maximize the use of this insulating liquid. Technology involving nano-modification can enhance the properties of these ester liquids placing them in a better position as a good alternative. In this study, we aim to investigate the performance of methyl ester liquid from neem seed oil, a non-food grade oil, when modified with SiO 2 insulating nanoparticles at several concentrations. Furthermore, Kraft paper compatibility has also been analyzed to observe their compatibility after impregnation. The breakdown strength, temperature-dependent dissipation loss factor, dielectric constant, and viscosity of both pure and nanofluid samples of neem ester liquid have been measured per standard methods. The nanofluid's dielectric performance has been significantly enhanced by the presence of SiO 2 nanoparticles according to results obtained from the experiment. For instance, the dielectric dissipation factor at an operating frequency of 60 Hz has been improved by 96%, 89%, and 79% at 30 °C, 50 °C, and 70 °C respectively relative to the base neem liquid. This remarkable improvement has been attributed to the SiO 2 nanoparticle's ability to polarize, trap, and de-trap mobile electrons develop in nanofluid when an electric field is applied. [ABSTRACT FROM AUTHOR]

The accurate identification of the oil-paper insulation state of a transformer is crucial for most maintenance strategies. This paper presents a multi-feature comprehensive evaluation model based on combination weighting and an improved technique for order of preference by similarity to ideal solution (TOPSIS) method to perform an objective and scientific evaluation of the transformer oil-paper insulation state. Firstly, multiple aging features are extracted from the recovery voltage polarization spectrum and the extended Debye equivalent circuit owing to the limitations of using a single feature for evaluation. A standard evaluation index system is then established by using the collected time-domain dielectric spectrum data. Secondly, this study implements the per-unit value concept to integrate the dimension of the index matrix and calculates the objective weight by using the random forest algorithm. Furthermore, it combines the weighting model to overcome the drawbacks of the single weighting method by using the indicators and considering the subjective experience of experts and the random forest algorithm. Lastly, the enhanced TOPSIS approach is used to determine the insulation quality of an oil-paper transformer. A verification example demonstrates that the evaluation model developed in this study can efficiently and accurately diagnose the insulation status of transformers. Essentially, this study presents a novel approach for the assessment of transformer oil-paper insulation. [ABSTRACT FROM AUTHOR]

This paper explores the potential for a system to be a co-author on an academic paper based on the criteria proposed by the International Committee of Medical Journal Editors (ICMJE). We used a third generation generative pretrained transformer (GPT-3) to write a review paper on the topic of its choice: The Effects of Sleep Deprivation on Cognitive Function. The system was asked to fulfill the four main criteria for co-authorship as recommended by ICMJE, which includes contributions to the conception or design of the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; and agreement to be accountable for all aspects of the work. Our results showed that the system was able to fulfill the criteria, with significant difficulties with accurate and reliable referencing. We also explored the ethical implications of using AI systems for research and found that it is important to take a cautious approach when considering its use for scientific authorship. This case study provides a methodology for further investigations into the possibilities and limitations of automated writing. [ABSTRACT FROM AUTHOR]

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]

High voltage connections will discharge through inductive voltage and combined transformers when they are switched off. In the past, there was a wide margin between the required capabilities and the actual capabilities of these components. Nowadays, however, this aspect is becoming more prominent due to a higher penetration by cable systems into the grid. Currently, none of the relevant international standards contain any definitions or requirements for line discharge, nor test methods which would validate the ability of the transformer to discharge a line of certain capacitance at a certain voltage. This is why the aim of this paper is to provide a foundation for how to specify, assess and verify the capacitive discharge performance of instrument transformers. This paper will present the basic theory, influencing parameters, multiple test sequences and acceptance criteria for cable discharge. It is clear that capacitive discharges present both thermal and mechanical stress to the unit, which is why this paper will detail how to guarantee both aspects. The idea is that this paper serves as a literal blueprint which can then be transferred to the relevant clauses in upcoming international standards. All conclusions within this paper are achieved through calculations and testing on actual 123 kV inductive voltage transformers and combined units. [ABSTRACT FROM AUTHOR]

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]

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]

High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover,most existingmethods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method based on a lightweight Transformer named RLAT, which consists of rotary position encoding, local-aggregated attention unit (LAU), and lightweight feedforward neural network (LW-FFN). Rotary position encoding is utilized to embed the relative position information for the HRRP sequence. Local aggregation attention unit can effectively aggregate and extract local features by local group linear transformation, and then the self-attention mechanism is adopted for perception and enhancement of global information. Thereby, the enhanced features are extracted by lightweight FFN. In addition, this paper adopts Label Smoothing regularization to add noise to the sample labels, which can improve the generalization performance of themethod. Finally, the effectiveness of the proposed method in real scenes is verified based on the MSTAR dataset, a real-world dataset for radar target recognition. Experimental results show that the proposed method achieves superior recognition performance compared to other remarkable methods and achieves significant generalization performance and robustness under variant sample and limited sample conditions. RLAT achieved an accuracy of 99.86% on the MSTAR standard dataset and 99.73% on the MSTAR variant dataset. In particular, it achieves an accuracy of 95.83% with only 274 training samples. Furthermore, the proposed method is more lightweight, with 90.90% reduction in the number of parameters and 96.70% reduction in the computation compared to the Vanilla Transformer, which facilitates deployment in edge devices. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the major development directions of large‐scale photovoltaic (PV) power system. In order to achieve low‐cost, high‐efficiency and long‐distance transmission of PV power, this paper adopted a DC grid‐connected topology by using multi‐modular cascaded DC‐DC converters, forming an input‐independent and output‐series (IIOS) system. In this system, an LLC type DC‐DC converter based on IGCT and magnetically integrated transformer was proposed in this paper, which can significantly increase the capacity, efficiency and power density of DC‐DC converter. And the equalization unit based on half‐bridge topology was adopted for the case of uneven irradiation of the PV inputs. In addition, this paper proposed an equalization control method based on the Nearest Level Modulation (NLM), which greatly simplifies the design of the acquisition and control system. Furthermore, the parametric design of a 50MW PV system was presented, and the simulation was carried out to verify the feasibility and advantages of the system in this paper. The proposed IIOS system is more suitable for high‐capacity, long‐distance PV power transmission, which can serve as a reference for the development of large‐scale photovoltaic systems. [ABSTRACT FROM AUTHOR]

One of the main reasons for the failure of transformer bushings is moisture penetration. Here, an online monitoring method of moisture in transformer bushings based on an improved humidity indicator card, which belongs to the group of passive physical and chemical monitoring methods, is proposed. First, the operating principles and monitoring system are explained. Next, by means of a moisture simulation device, the moisture migration characteristics between the gas and oil, the influence of the interface radius on the response characteristics of the monitoring device, and the characteristics of the humidity indicator card are studied experimentally. Then, the moisture migration process in micro‐scale is simulated by molecular dynamics simulation. Finally, by changing the mass fraction of the cobalt chloride in the humidity card, the warning values of relative humidity at different temperatures are obtained. The simulation results show that water molecules can migrate from the oil–paper system to the air. Moreover, once the moisture enters the transformer bushing, the proposed humidity indicator card can correctly display the relative humidity at different temperatures. In conclusion, the proposed method provides a new way for online moisture monitoring of transformer bushings. [ABSTRACT FROM AUTHOR]

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]

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]