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Herein, a multifactor optimization of typical switchgear circuit diagrams for substations with voltages up to 220 kV was performed considering reliability, cost-effectiveness, ease of operation, environmental friendliness, and compactness. Each factor was presented in its own "coordinate system," that is, regardless of subjective expert assessments in terms of the values of the weighting coefficients of each influencing factor. The solution to the problem was based on the concept of optimality according to Edgeworth – Pareto. The regions of Pareto-optimal schemes were narrowed using the ideal point (utopia point) method. The stability of the decisions taken was analyzed when the initial data varied. The existing regulated nomenclature of typical switchgear circuits for substations with voltages up to 220 kV could and should be reduced from eight to three: circuit 9N (with one sectioned collecting busbar system and with connection of [auto]transformers through a fork of switches), circuit 12 (with one sectioned collecting busbar system and a bypass busbar system), and circuit 16 (busbar transformers with one-and-a-half line connections). The unification of circuits will have a positive effect on the reliability and cost-effectiveness of electrical networks. [ABSTRACT FROM AUTHOR]

Medium-Voltage (MV) electrical switchgear is some of the essential equipment for ensuring the stability of electrical networks. However, detecting mechanical faults in these systems, especially in light of current operations and safety concerns, is a complex task. This paper presents a useful method that uses a one-dimensional convolution neural network (1D-CNN) to find both mechanical and non-mechanical faults in MV electrical switchgear with a high level of accuracy. The identification of mechanical faults in MV electrical switchgear requires accuracy and speed to avoid aggravation of the faults and the occurrence of risks. The proposed 1D-CNN model proved to work very well in identifying spatial features. Thus, by combining time domain and frequency domain analysis, it not only refines the classifier but also increases the fault detection probability in real-world situations. Even during real-time signal pre-processing and model validation, our model was able to predict the incurred faults for the scenarios in the time and frequency domains with 100% accuracy. The study provides an original approach that increases the efficiency of fault identification and the reliability of MV electrical switchgear at the same time. Introducing our 1D-CNN model, we advance the field of fault detection by minimizing downtime and maximizing maintenance effectiveness, in both time and frequency domain analyses. [ABSTRACT FROM AUTHOR]

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F1-Scores between 83.11% and 98.34% in the pre-trained architectures.

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

Copyright of Scientific Papers of Electrical & Control Engineering Faculty Technical University of Gdansk / Zeszyty Naukowe Wydzialu Elektrotechniki & Automatyki Politechniki Gdanskiej is the property of Gdansk University of Technology, Faculty of Electrical & Control Engineering 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.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance.

Abstract The explosion accident due to the internal arc fault in the metal‐enclosed switchgear cannot be ignored. Therefore, improving the ability of switchgear to resist the arc fault has important significance. In this paper, a passive protection measure by installing the buffer volume (BV) was proposed. The short‐circuit arcing tests were carried out in the small‐scale switchgear compartments, and the pressure rise was measured in the different models. The changes of pressure rise before and after installing BV were analyzed. Then, the calculation methods of pressure rise in the compartments and the mechanical response of the enclosure were put forward. By calculating the distributions of the pressure rise and temperature rise in the compartments, as well as the time‐varying displacement and stress of the enclosure, the pressure relief effect of the BV has been discussed. Results show that after installing the BV, the pressure rise, displacement, and stress are all decreased obviously in the initial of arcing. Hence, the arc fault protection effect for the switchgear can be improved by properly installing the BV.

If an R.M.U. (Ring Main Unit) cubicle is installed, operated and maintained in accordance with current standards and the manufacturer's instructions, the likelihood of internal arcing is reduced, but should not be completely ignored. This article outlines the experimental situations in which arcing can occur and the catastrophic effects seen if R.M.U. is not properly designed for this type of damage. This type of failure can be caused by a fault, abnormal operating conditions or a malfunction and represents a danger during its installation and to persons present. The situations in which this type of failure can occur are presented and experimentally analysed: bi-phase internal arcing in the connections compartment, three-phase internal arcing in the switching compartment, internal arcing in the fuse compartment and single-phase arcing between a phase and the switchgear enclosure. [ABSTRACT FROM AUTHOR]

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge. [ABSTRACT FROM AUTHOR]

The multi-source monitoring data of switchgear contains rich equipment operating status information, and analyzing it can achieve switchgear fault diagnosis. A fault diagnosis method for switchgear based on SMOTE-SSA-CNN was proposed. Firstly, based on monitoring data such as switchgear voltage, current, and temperature and humidity, the synthetic minority over-sampling technique SMOTE) algorithm was used to expand the original dataset, solving the problem of severe imbalance between positive and negative samples in the original dataset. Then, the sparrow search algorithm SSA) was introduced to optimize the hyperparameters of convolutional neural networks CNN), such as the size and number of convolutional kernels, the number of fully connected layer neurons, and the learning rate, in order to improve the accuracy of the model's fault diagnosis results. Finally, the performance of the established SMOTE-SSA-CNN model was evaluated through example analysis, verifying the effectiveness of the proposed method for switchgear fault diagnosis. Compared with traditional fault diagnosis methods,the proposed method has better convergence and higher accuracy. [ABSTRACT FROM AUTHOR]

The explosion accident due to the internal arc fault in the metal‐enclosed switchgear cannot be ignored. Therefore, improving the ability of switchgear to resist the arc fault has important significance. In this paper, a passive protection measure by installing the buffer volume (BV) was proposed. The short‐circuit arcing tests were carried out in the small‐scale switchgear compartments, and the pressure rise was measured in the different models. The changes of pressure rise before and after installing BV were analyzed. Then, the calculation methods of pressure rise in the compartments and the mechanical response of the enclosure were put forward. By calculating the distributions of the pressure rise and temperature rise in the compartments, as well as the time‐varying displacement and stress of the enclosure, the pressure relief effect of the BV has been discussed. Results show that after installing the BV, the pressure rise, displacement, and stress are all decreased obviously in the initial of arcing. Hence, the arc fault protection effect for the switchgear can be improved by properly installing the BV. [ABSTRACT FROM AUTHOR]

The basic requirements for the design of a switchgear for main 35–220 kV step-down substations are reliability and efficiency. Switchgear circuits are chosen depending on the number of supply and transit lines and transformers, the substation's place in the power supply system, and the area that can be allocated under the substation construction. The substation switchgear's reliability depends on the circuit type and the equipment to be installed. When designing, as a rule, standard substation switchgear circuit solutions are chosen with often unreasonably overstated reliability indicators and, as a consequence, capital costs. This paper explores the issues of automated choice of the best option for the 35–220 kV substation switchgear circuit, considering its structural reliability and additional costs for the area allocated under the electrical unit. A distinctive feature of the work is a comprehensive accounting of the reliability indicators of the substation and the influence of the layout of switchgear circuits as a whole on the performance indicators of future electrical installations. The developed technique is used at the pre-design stages and allows for reducing further capital, maintenance, and repair costs for electrical units by up to 14.5%. The developed approach is implemented in the original TER CAD software product. [ABSTRACT FROM AUTHOR]

Abstract The restrike of vacuum circuit breaker during the breaking process causes overvoltage and intensifies the insulation deterioration of high‐voltage equipment. To solve this problem, the vacuum breaker aging test method and the restrike characteristics are researched. A withstand voltage test platform of 40.5‐kV vacuum circuit breaker was established to obtain the effects of the contact spacing and loop and inrush currents of vacuum circuit breaker on the number of restrike, the wavefront steepness of the breakdown voltage, and the breakdown duration. The number of restrike during the breaking of vacuum circuit breaker decreased and the duration of the repeated‐breakdown arc increased with the increased contact spacing of vacuum circuit breaker. As the loop current of the test system increased, the number of restrike during the breaking of vacuum circuit breaker, the wavefront steepness of the breakdown voltage, and the duration of the restrike arc increased. As the inrush current amplitude increased during the closing of vacuum circuit breaker, the number of restrike and the duration of the repeated‐breakdown arc during the breaking of vacuum circuit breaker increased. The results provide a theoretical basis and data support for the preventive test of 40.5‐kV vacuum circuit breaker and the optimization of the withstand voltage test.

The article presents the results of laboratory measurements of Ud breakdown voltages in a high-voltage vacuum insulating system for different pressures, contact gaps, type of electrode contacts and type of residual gas inside the discharge chamber. First of all, the electrical strength of the discharge chamber with a contact system terminated with contact pads made of W 70Cu 30 and Cu 75Cr 25 material was compared for selected values of contact gaps. It was found that below a pressure of p = 3.0 x 10 -1 Pa the electrical strength reaches an approximately constant value for each of the set contact gaps d. Analytical relationships were determined to calculate this value for each of the contact pads used. Above a pressure of p = 3.0 x 10 -1 Pa, the measured values of Ud breakdown voltages decrease sharply. The values of breakdown voltages in the discharge chamber with residual gases in the form of air, argon, neon and helium were also determined for selected values of contact gaps d. Depending on the residual gases used, significant differences were noted in the values of pressure p at which the loss of insulating properties in the discharge chamber occurred. These values were 3.3 x 10 -1 Pa for argon, 4.1 x 10 -1 Pa for air, 6.4 x 10 -1 Pa for neon, and 2.55 x 10 0 Pa for helium, respectively.

The significance of a dependable and sufficient electrical power supply in societal development cannot be overstated. The distribution of electrical power holds a crucial position in the power system chain, representing the final stage where it reaches consumers. At this phase, various types of losses, including technical loss and theft, among others are associated. The Ile-Oluji community is situated in Ondo State, a south-western part of Nigeria, A region which is host to federal, state, local and private institutions. This community grapples with inconsistent power supply experiencing intermittent trips without a clear originating cause from components in the Ile-Oluji injection substation and its feeder in Ondo. This study analyses the power distribution patterns in this locality focusing on three primary elements (transformer, switch gear and supply line) extracting data on; failures, outage time, numbers of customers and total hours for the year 2019 to 2022. Reliability indices were employed to assess its performance utilising the MATLAB software. Codes were crafted to extract these indices; Availability, Failure rates, Mean Time to Repair (MTTR), Mean Time Between failure (MTBF), System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI). The results show that there is a strong relationship among the reliability indices. It is adduced that the lower the failure rate, the higher the availability of a system. It was concluded that the power system switchgear recorded the highest failure rate amongst the three elements with 0.037341 hrs/year followed by supply line with 0.022541 hrs/year and the transformer with 0.0148 hrs/year in 2022. In corollary, transformer has the highest availability of 0.94441 followed by supply line with 0.81681 and switchgear with 0.78004 in 2021, switchgear recorded the highest failure rates because it is responsible for executing both forced and planned outages.

In this study, we developed a conversion core that produces power by utilizing the unused magnetic field in a switchboard. The conversion core makes it possible to utilize power that is normally wasted. The conversion core is composed of a core, filter, and battery. A prototype was installed in a switchboard to conduct tests on the output, battery storage, and output boosting of multiple batteries. Energy was harvested from the magnetic field generated by a busbar of the switchboard, and the power conversion ratio of the core yielded 1.08-1.01 mW per 1 A of bus current. Supplying this technology to the market after further R&D and commercialization is expected to greatly assist in the dissemination of energy harvesting, which has not yet spread widely to the general public. [ABSTRACT FROM AUTHOR]

To improve the insulation diagnosis of a medium-voltage (MV) switchgear, we have developed a four-terminal surface current sensor and a method to estimate the direction of partial discharge occurrence. In this method, the accuracy of the direction estimation is improved by referring only to the direct wave, and the error due to noise can be suppressed by calculating the outputs of the four terminals. The experimental results demonstrate that the detection characteristics of the sensor concur well with the theoretical characteristics derived from the detection principle. Additionally, we conducted an experiment using the developed sensor and the new method to detect the surface current generated due to partial discharge occurring in a rectangular enclosure. The results demonstrate that the occurrence direction could be estimated even for a rectangular enclosure. Furthermore, the propagation attenuation of the surface current at the corners of the enclosure was large, exhibiting an attenuation 0.25 times for the worst case. Finally, it was confirmed that noise removal using a wavelet transform could reduce the direction estimation error of the discharge occurrence by approximately half. [ABSTRACT FROM AUTHOR]

The restrike of vacuum circuit breaker during the breaking process causes overvoltage and intensifies the insulation deterioration of high‐voltage equipment. To solve this problem, the vacuum breaker aging test method and the restrike characteristics are researched. A withstand voltage test platform of 40.5‐kV vacuum circuit breaker was established to obtain the effects of the contact spacing and loop and inrush currents of vacuum circuit breaker on the number of restrike, the wavefront steepness of the breakdown voltage, and the breakdown duration. The number of restrike during the breaking of vacuum circuit breaker decreased and the duration of the repeated‐breakdown arc increased with the increased contact spacing of vacuum circuit breaker. As the loop current of the test system increased, the number of restrike during the breaking of vacuum circuit breaker, the wavefront steepness of the breakdown voltage, and the duration of the restrike arc increased. As the inrush current amplitude increased during the closing of vacuum circuit breaker, the number of restrike and the duration of the repeated‐breakdown arc during the breaking of vacuum circuit breaker increased. The results provide a theoretical basis and data support for the preventive test of 40.5‐kV vacuum circuit breaker and the optimization of the withstand voltage test. [ABSTRACT FROM AUTHOR]

Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective. [ABSTRACT FROM AUTHOR]

Pre-insertion resistor (PIR) is an important component of circuit breaker. The healthiness of the circuit breaker (CB) depends upon that of PIR contact. With the increasing demand of uninterrupted power supply, failure in major equipment like CB may incur huge loss to the utility. The existing study reveals that mechanical failures are more dominant than other types of failures. Thus, many industries prefer to undertake preventive maintenance of equipment rather than break down maintenance. This paper describes the actual problem detected in PIR contact of North Eastern Regional Transmission System, having a 400 kV, 3000 A, SF6 CB through testing and rectification thereafter. The results obtained from the graph helped in detecting mechanical dis-alignment of the connecting link in the link arm and the tubular contact of the PIR. Furthermore, a model has been developed to show the possible threat that would have occurred, had the problem been not rectified. This paper will help the utilities to emphasize on detecting the problem by undergoing preventive maintenance and thereby saving unprecedented failure of equipment. [ABSTRACT FROM AUTHOR]

The article presents the results of laboratory measurements of Ud breakdown voltages in a high-voltage vacuum insulating system for different pressures, contact gaps, type of electrode contacts and type of residual gas inside the discharge chamber. First of all, the electrical strength of the discharge chamber with a contact system terminated with contact pads made of W70Cu30 and Cu75Cr25 material was compared for selected values of contact gaps. It was found that below a pressure of p = 3.0x10-1 Pa the electrical strength reaches an approximately constant value for each of the set contact gaps d. Analytical relationships were determined to calculate this value for each of the contact pads used. Above a pressure of p = 3.0 x 10-1 Pa, the measured values of Ud breakdown voltages decrease sharply. The values of breakdown voltages in the discharge chamber with residual gases in the form of air, argon, neon and helium were also determined for selected values of contact gaps d. Depending on the residual gases used, significant differences were noted in the values of pressure p at which the loss of insulating properties in the discharge chamber occurred. These values were 3.3 x 10-1 Pa for argon, 4.1 x 10-1 Pa for air, 6.4 x 10-1 Pa for neon, and 2.55 x 100 Pa for helium, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the operational reliability of different types of switching substations within the context of power systems, employing the Monte Carlo method for analysis. The research focuses on evaluating the reliability of high-voltage substations, including single-busbar systems, double-busbar systems, and switchgears with a ring-type power supply. By conducting simulations and analyzing statistical data on device reliability, the study aims to identify the most reliable implementation of switching substations. The results are presented through graphical representations and comparative tables, highlighting the impact of factors such as the number of switching elements and their connection on operational reliability. The findings indicate that configurations with a greater number of busbars and a parallel connection of switching elements exhibit higher operational reliability. The study provides insights to inform decision-making in the construction of new switching substations, emphasizing the importance of stable operation within power systems.

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office 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.)

Traditional switchgear current measurement techniques use closed-loop current modules to accurately measure and control current, and in switchgear, there is also interphase interference, these modules usually require a large core that surrounds the entire busbar as well as a shielded shell, because the compensation current generated inside the module is proportional to the busbar current, so the power consumption can be as high as several watts. In view of this, this paper proposes a magnetic sensor-based switch cabinet busbar current measurement method, and through theoretical deduction, it is determined that the magnetic sensor with two magnetic sensitive directions opposite and vertically placed in the busbar can eliminate the interference from the busbar phase and the stray field. Through simulation analysis, it was determined that the opening hole of the switch cabinet bus row was a circular hole, and the placement of the two magnetic sensors was at the edge of the circular hole, so that the practical application of the method in the switch cabinet had certain guiding significance. [ABSTRACT FROM AUTHOR]

In the authors' previous research, a new method for partial discharge detection in the switchgear based on near-field detection was proposed. The content of this paper is the continuation of the authors' previous research. In order to realize the reasonable layout of the near-field magnetic field probe for partial discharge detection in the switchgear, this paper simulates and analyzes the influence of the internal structure of the switchgear on the near-field propagation characteristics of the electromagnetic wave signal generated by partial discharge, and determines the installation position of the near-field probe in the switchgear. Firstly, the propagation characteristics of electromagnetic wave signals in the different media of the switchgear are analyzed, and the switchgear model is established. Then, based on the finite difference time domain method, the influence of different devices in the switchgear on the near-field propagation of the partial discharge electromagnetic wave signal is simulated. The simulation results show that the current transformer, insulator, busbar and cabinet all obviously attenuate the amplitude of the near-field electromagnetic wave signals generated by partial discharge, and the insulator causes obvious signal distortion. Finally, it is determined that the near-field probe can be installed on the inner wall or the right wall near the bottom plate of the switchgear. [ABSTRACT FROM AUTHOR]

Switchgear and control gear are susceptible to arc problems that arise from slowly developing defects such as partial discharge, arcing, and heating due to faulty connections. These issues can now be detected and monitored using modern technology. This study aims to explore the effectiveness of deep learning techniques, specifically 1D-CNN model, LSTM model, and 1D-CNN-LSTM model, in detecting arcing problems in switchgear. The hybrid model 1D-CNN-LSTM was the preferred model for fault detection in switchgear because of its superior performance in both time and frequency domains, allowing for analysis of the generated sound wave during an arcing event. To investigate the effectiveness of the algorithms, experiments were conducted to locate arcing faults in switchgear, and the time and frequency domain analyses of performance were conducted. The 1D-CNN-LSTM model proved to be the most effective model for differentiating between arcing and non-arcing situations in the training, validation, and testing stages. Time domain analysis (TDA) showed high success rates of 99%, 100%, and 98.4% for 1D-CNN; 99%, 100%, and 98.4% for LSTM; and 100%, 100%, and 100% for 1D-CNN-LSTM in distinguishing between arcing and non-arcing cases in the respective training, validation, and testing phases. Furthermore, frequency domain analysis (FDA) also demonstrated high accuracy rates of 100%, 100%, and 95.8% for 1D-CNN; 100%, 100%, and 95.8% for LSTM; and 100%, 100%, and 100% for 1D-CNN-LSTM in the respective training, validation, and testing phases. Therefore, it can be concluded that the developed algorithms, particularly the 1D-CNN-LSTM model in both time and frequency domains, effectively recognize arcing faults in switchgear, providing an efficient and effective method for monitoring and detecting faults in switchgear and control gear systems. [ABSTRACT FROM AUTHOR]

The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains. [ABSTRACT FROM AUTHOR]

In view of the fact that the partial discharge (PD) signal energy is mainly concentrated below hundreds of megahertz, the ultra-high frequency part of the energy is weak, and the interior space of the switchgear is narrow, this paper proposes a new method for PD detection of the switchgear based on near-field detection. Firstly, based on the principle of PD, the field characteristics of the signal in the switchgear are analyzed. After that, the probe is designed with an electric small loop structure. Based on its equivalent circuit, its measurement principle and amplitude frequency characteristics are analyzed. The influence of probe size and material on amplitude frequency characteristics is obtained by using simulation software High Frequency Structure Simulator (HFSS), and the probe parameters suitable for PD detection in the switchgear are determined. Finally, the performance of the probe is measured by network analyzer, and the PD signal is tested on the simulated PD test platform. The results show that the probe works in the frequency band of 10–200 MHz and can receive PD signals containing more energy information. In the operating frequency band, the reflection coefficient of the probe port is very large, and its interference to the signal near field is particularly small. The probe also has good frequency response characteristics, and the fluctuation in the frequency band is less than 5 dB, which can obtain more accurate PD signal characteristics in subsequent processing. In addition, the probe is passive, with dimensions of 166 mm in length, 104 mm in width, and 2 mm in thickness, which is suitable for placing in the switchgear with small internal space. The results of PD receiving test show that the probe can reflect the occurrence of PD remarkably and accurately. [ABSTRACT FROM AUTHOR]

The main goal of the present study is to carry out one-way coupled computations of electromagnetics and heat transfer for a simplified version of a switchgear and to reveal the applicability of the computational approach. In our computation, we introduce the joule losses that we obtain from the electromagnetic computations to a thermal solver as heat load. In order to reach the goal, we compare the temperature values that we obtained at eight different measurement points by performing computations and experimental measurements. In general, the distributions of the obtained temperature values are quite similar. The notable difference in the mean values of the obtained temperatures might result from limitations of one-way coupled approach where we have to neglect temperature dependency of the electrical properties during the electromagnetic computations. [ABSTRACT FROM AUTHOR]

Abstract High‐voltage direct current breaker is among the essential components of high‐voltage direct current grids. Such a breaker generally needs a direct current reactor to reduce the fault currents rate. However, direct current reactors have destructive effects on the multi‐terminal high‐voltage direct current grid dynamic stability, and in such a system, despite the variety of controllers, the system dynamics are highly sensitive to the operating point. Therefore, additional damping control will be needed. This paper proposes a modification to be applied to the traditional droop controller of high‐voltage direct current grids to cope with the influence of these large reactors, improving the direct voltage stability and decreasing power variations in the transient events by introducing a direct current power system stabilizer. The proposed method for direct voltage control has been investigated through the analytical model of the system. Stability improvement has been studied following the application of the proposed method by investigating zeros, poles, and frequency response analysis. Moreover, a method is proposed for optimal design and optimal placement of direct current power system stabilizer. The system analysis and time‐domain simulations demonstrate a decent damping improvement attained by the proposed method. All simulations and analytical studies are conducted on Cigré DCS3 test high‐voltage direct current grid in MATLAB/Simulink.

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Abstract Recently, numerous data‐driven fault diagnosis methods have been developed, and the tasks involving the same distribution of training and test data have been well solved. However, considering the particularity of gas‐insulated switchgear (GIS), collecting massive data, especially with the same distribution, is difficult. Therefore, existing fault diagnosis methods hardly achieve satisfactory insulation defect diagnosis with small datasets. Aiming at solving this problem, a novel domain adversarial transfer convolutional neural network (DATCNN) is proposed, realising the diagnosis of GIS insulation defects on small samples. First, a residual CNN is built to learn feature representations from the source and target domains. Second, the domain adversarial training strategy is used for feature transfer, where a conditional adversarial mechanism is introduced, and the joint distribution of features and labels is improved to a random linear combination, which realises the simultaneous adaptation of features and labels. Finally, the Nesterov accelerated gradient descent optimisation algorithm is used to speed up the gradient convergence. DATCNN has 99.15% and ≥89.5% diagnosis accuracy for GIS insulation defects in the laboratory and on‐site, respectively. Comprehensive experiment results show the effectiveness and superiority of the proposed method in diagnosing GIS insulation defects with small samples.

Abstract A protection scheme is proposed using alienation method derived from coherence model which is a backup protection method for the differential overcurrent protection of synchronous generators. This scheme which is realized by the computational technique based on the alienation coefficients estimated for generator voltage and current signals is considered to be mounted beside the main differential protection system, which operates in the cases that differential protection is unable to detect the internal faults. This novel protection algorithm operates by adjusting the alienation settings of the suggested relay that sends the trip signal to the three‐phase circuit breaker of the protected generator. In the proposed method, fault occurrences can be determined regardless of variations of terminal voltage and current in different working states of synchronous generator. This is done by designing new quadratic‐tripping characteristics based on alienation limits for the electrical signals. To demonstrate the proposed method, a motor‐synchronous generator set with built instrument transformers, for measuring voltage and current data at the generator terminals, are used. Extensive experimental investigations validate the functionality of this novel protection scheme under different abnormal, unbalance and operating conditions for the generator under test.

Abstract Aiming at the fast protection requirements of electrical ships LVDC distribution system, this paper studies a topology of unidirectional vacuum hybrid circuit breaker. The hybrid circuit breaker is an improved current injection LVDC breaker based on precharged capacitor, which improves the dielectric recovery capability of vacuum interrupter after artificial current zero by using a freewheel diode. A synthetic test platform is built to study the dielectric strength recovery characteristics of a 45 mm diameter CuCr50 butt contact vacuum interrupter (driven by Thomson coil actuator, average opening speed of about 3 m/s)under high current(about 20 kA) and small gap(about 1 mm). The experimental results indicate that the dielectric strength of the contact can recover at a very fast speed within the arc time of 100 µs; when the arc time is increased to 320 µs, the dielectric strength of the contact cannot recover for a long time. Therefore, the design principle of the circuit breaker is obtained: within the arc time of 100 µs, by increasing the arc time can increase the contact distance, so the transient interruption voltage tolerance margin of the circuit breaker can be improved. Based on the results of the test, this paper also proposes a calculation method of forced commutation circuit parameters, which is verified by the simulation. The research results show that the DC circuit breaker can realise the rapid breaking of 20 kA fault current within 2 ms, and has a good application prospect in the LVDC distribution system.

Abstract This paper proposes an effective fault management scheme for medium‐voltage direct current (MVDC) networks, which includes a virtual‐impedance‐based fault current limiter (VI‐FCL) on the DC side and a positive‐negative‐sequence (PNS) control scheme on the AC side. Depending on the fault current value, different protection strategies are adopted, which include either isolating the fault by opening circuit breakers or riding through the fault with the proposed VI‐FCL. It is known that unbalanced grid voltage can be found in conventional AC distribution feeders due to unbalanced loading conditions among three phases. The unbalanced grid voltage could induce double line‐frequency (2ω) ripples on both DC voltage and current, which could thereby trigger protection malfunction in the DC sub‐sections. Therefore, the PNS control scheme is used to eliminate the 2ω ripples of both DC voltages and currents caused by unbalanced AC voltages. Furthermore, a detailed 2ω mathematical model of the MVDC network under unbalanced AC voltage condition is derived to investigate how the 2ω ripple propagates across the network. A voltage source converter based multi‐terminal MVDC network is built to test DC faults with different fault currents, unbalanced AC voltages, and different control schemes. The test results verify both the proposed fault management scheme and the 2ω ripple propagation analysis.