2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)
The critical step in the mechanical fault diagnoses of a high-voltage circuit breaker (HVCB) is t... more The critical step in the mechanical fault diagnoses of a high-voltage circuit breaker (HVCB) is the extraction of mechanical state characteristics. The effectiveness of these characteristics is related to the accuracy of the corresponding diagnostic results. A novel method for the mechanical fault feature extraction of a HVCB via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and weighted time-frequency entropy is proposed in this study. First, the time-frequency decomposition of vibration signals is performed by the CEEMDAN and the Hilbert transform to obtain the instantaneous frequency of intrinsic mode function (IMF) components. Then, the IMFs are reconstructed via a bandpass filter to gain a signal component of the specified frequency band. In addition, the weighted time-frequency entropy feature is extracted from the energy matrix. Through the verification of multiple groups of classifiers, the diagnostic results have a higher accuracy, which indicates that the proposed method can effectively characterize different mechanical faults.
2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE)
Data-driven fault diagnosis of high impedance fault (HIF) has received increasing attention and a... more Data-driven fault diagnosis of high impedance fault (HIF) has received increasing attention and achieved fruitful results. However, HIF data is difficult to obtain in engineering. Furthermore, there exists an imbalance between the fault data and non-fault data, making data-driven methods hard to detect HIFs reliably under the small imbalanced sample condition. To solve this problem, this paper proposes a novel HIF diagnosis method based on conditional Wasserstein generative adversarial network (WCGAN). By adversarial training, WCGAN can generate sufficient labeled zero-sequence current signals, which can expand the limited training set and achieve the balanced distribution of the samples. In addition, the Wasserstein distance was introduced to improve the loss function. Experimental results indicate that the proposed method can generate high-quality samples and achieve a high accuracy rate of fault detection in the case of small imbalanced samples.
Iet Generation Transmission & Distribution, Dec 1, 2019
A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault curre... more A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault current when a single-phase-to-ground fault occurred in distribution networks. However, passive ASDs cannot compensate for the active and harmonic components of ground-fault current. Furthermore, a massive residual ground-fault current would endanger human life and the reliable operation of distribution networks. To overcome the shortcomings of passive ASDs, a three-phase flexible ASD (FASD) with a three-phase cascaded H-bridge topology was presented. A three-phase FASD connected to a bus bar of distribution networks was employed to compensate for the ground-fault current. In this study, the FASD with the backstepping control (BSC) based on current-compensation principle was proposed to completely eliminate the ground-fault current. The effectiveness of the proposed FASD with the BSC method was verified using the simulation and experimental results.
In practice, the performance of distribution feeder parameter estimation is limited by the measur... more In practice, the performance of distribution feeder parameter estimation is limited by the measurement conditions in distribution networks. An accurate mathematical model that considers limited phasor measurements in distribution networks is necessary to estimate feeder parameters. This paper presents a set of modified parameter estimation models for unbalanced three-phase distribution feeders that only require the measurements of voltage amplitudes and power flows. To simplify the calculation process and improve the estimated results, a method combined with a radial basis function neural network (RBFNN) and multi-run optimization method (MRO), namely RBFNN-MRO, is proposed to calculate the parameters of distribution feeders. The relationship between the feeder parameters and the measurement data from the two terminals of the feeder can be mapped perfectly using the RBFNN. Furthermore, the random errors in the measurement device were eliminated using the proposed RBFNN-MRO algorithm. The RBFNN-MRO algorithm can limit the number of neurons in the hidden layer and substantially reduce the training time for each RBFNN. The feasibility of the proposed method was verified using four IEEE test systems. The proposed RBFNN-MRO and RBFNN methods were compared using the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN-MRO method has excellent potential for improving the accuracy of feeder parameter estimation even without synchronized phasor measurement. INDEX TERMS Parameter estimation method, radial basis function neural network (RBFNN), nonlinear relation, three-phase distribution feeder, maximum absolute percentage error (MAPE).
2018 China International Conference on Electricity Distribution (CICED), 2018
Peterson coil has not achieved excellent arc-suppression performance due to the increased harmoni... more Peterson coil has not achieved excellent arc-suppression performance due to the increased harmonic and active component of the fault current with the expansion and development of distribution networks. To address the problem, a flexible arc-suppression device (FASD) based on the three-phase cascaded H-bridge (CHB) converter has been proposed. This paper presents an interconnection strategy for the FASD in order to further improve the reliability of the device. When the FASD is under abnormal conditions such as line fault, incorrect fault-phase selection and excessive injection current, the proposed strategy achieves the interconnection of CHB converters between different phases to guarantee the uninterruptable operation of the arc-suppression function of the FASD. Simulation results are illustrated to verify the feasibility of the proposed interconnection strategy.
In recent years, many supervised learning algorithms have been successfully applied for photovolt... more In recent years, many supervised learning algorithms have been successfully applied for photovoltaic (PV) fault diagnosis. In practice, it is not possible to effectively obtain labels of large samples, limiting the engineering application of these algorithms. As for the unsupervised learning algorithm, it is completely adaptive learning, requiring a large number of samples to better learn the potential features in the data. To address the above problems, an improved online fault diagnosis method is proposed, which uses a small number of labeled samples to train the semisupervised ladder network (SSLN) fault diagnosis model to realize the diagnosis of line-to-line faults, open-circuit faults, partial shadow faults, and hybrid faults. In the proposed method, only the real-time operating voltage and current of PV array are needed for fault diagnosis. The sequential voltage and current of the PV array are first normalized, and the sequential power waveforms are obtained through numerical calculation. Then, the SSLN is used to extract the fault features from the sequence power waveforms. Finally, the classification is realized using the SSLN's noiseless encoder. To eliminate overfitting and improve convergence, the activation function, optimizer, and loss function of the SSLN is studied and improved. Meanwhile, numerical simulations and measured data verify that the proposed method provides strong anti-interference, and the diagnostic accuracies of both exceed 98%. Comparative experiments show that the proposed method outperforms algorithms such as squared-loss mutual information regularization, semisupervised support vector machine, graph-based semisupervised learning, and semisupervised extreme learning machine.
2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017
It is still a focus of research to detect the faulty feeder timely and accurately in resonant gro... more It is still a focus of research to detect the faulty feeder timely and accurately in resonant grounding distribution systems. The conventional methods commonly use single faulty feeder detection methods, such as wavelet transform method, transient energy method, and the fifth harmonic current method, etc. However, their reliability is not satisfied due to the partial fault information is considered. A novel approach to identify the faulty feeder based on discrete wavelet packet transform (DWPT) and machine learning is proposed in this paper. The time-frequency matrices are obtained by utilizing the DWPT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The feature vectors will be extracted manually by calculating time-frequency matrices with statistical quantities. The two classifiers (Adaboost+CART and SVM) are trained by a large number of feature vectors under various kinds of fault conditions and factors, respectively. The faulty feeder detection can be achieved by the trained two classifiers. A PSCAD/EMTDC simulator is established to simulate a practical 10-kV resonant grounding distribution system. Simulation results of the testing cases validate that the proposed approach of fault detection is able to achieve good identification accuracy.
2021 6th International Conference on Power and Renewable Energy (ICPRE), 2021
The treatment of the neutral point is essential for the distribution networks. The resonant groun... more The treatment of the neutral point is essential for the distribution networks. The resonant grounding system (RGS) is prone to generate the neutral point overvoltage caused by the low reactance of the arc suppression coil (ASC), which harms the operation of equipment and personal safety. The traditional passive neutral point overvoltage suppression strategies have poor adaptability, which do not meet the demands of increasing complexity of the distribution network lines. Therefore, we present a neutral point overvoltage suppression method based on backstepping control (BSC) with DC suppression. The BSC that prevents perturbation of system parameters achieves the neutral point overvoltage suppression. A DC suppression loop is constructed to realize the injection current DC suppression of the cascaded H-bridge (CHB) inverter, which is directly connected to the neutral point of the distribution networks. Simulation results verifies the effectiveness of neutral point overvoltage suppression based on BSC and the feasibility of DC suppression of injection current.
IEEE Transactions on Instrumentation and Measurement, 2021
Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in d... more Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order.
IEEE Transactions on Instrumentation and Measurement, 2021
In general, vibration signals generated by the switching operation of a high-voltage circuit brea... more In general, vibration signals generated by the switching operation of a high-voltage circuit breaker (HVCB) contains important information to reflect its mechanical status. A method for mechanical fault diagnoses of an HVCB based on a semisupervised stacked autoencoder (SSAE) and an integrated extreme learning machine (IELM) is proposed in this study. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain the time–frequency energy matrix. Then, an SSAE model is applied to automatically extract the characteristic information from the energy matrix. As a result, two-level classifiers can be constructed. The first level is utilized to identify normal or abnormal states, and the second level is selected to identify various types of faults in the abnormal state. The classifiers of these levels are composed of binary IELM. The advantages of the proposed method are that it not only can automatically extract the high-recognition features from the time–frequency energy matrix of high dimension to complete the identification of the existing fault types in the training set but also can accurately identify the samples of unknown types of faults. Experimental results show that the proposed method can effectively diagnose mechanical faults of an HVCB, and the classification accuracy reaches 99.5%.
IET Generation, Transmission & Distribution, 2019
A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault curre... more A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault current when a single-phase-to-ground fault occurred in distribution networks. However, passive ASDs cannot compensate for the active and harmonic components of ground-fault current. Furthermore, a massive residual ground-fault current would endanger human life and the reliable operation of distribution networks. To overcome the shortcomings of passive ASDs, a three-phase flexible ASD (FASD) with a three-phase cascaded H-bridge topology was presented. A three-phase FASD connected to a bus bar of distribution networks was employed to compensate for the ground-fault current. In this study, the FASD with the backstepping control (BSC) based on current-compensation principle was proposed to completely eliminate the ground-fault current. The effectiveness of the proposed FASD with the BSC method was verified using the simulation and experimental results.
International Journal of Electrical Power & Energy Systems, 2020
Because of the hazard of single-phase-to-ground faults on distribution networks for human securit... more Because of the hazard of single-phase-to-ground faults on distribution networks for human security and device insulation, it is important for the ground-fault current to be eliminated by an arc-suppression device. However, owing to their shortcomings, passive arc-suppression devices cannot compensate the ground-fault current fully. Therefore, active arc-suppression devices have been studied. The flexible arc-suppression device (FASD) with a cascaded H-bridge (HB) topology is one type of active arc-suppression device. An FASD connected to a bus bar of distribution networks is applied to mitigate the ground-fault current. Owing to the high voltage and large current during the ground fault, a large number of HBs are required for the FASD. To modulate the HBs, an improved distributed commutations modulation method, which is combined with the flexible arc-suppression method, is proposed. The proposed modulation strategy aims to minimise the error between the reference signal and the tracking signal during each sampling period. Finally, the proposed strategy is validated via simulations and experiments to confirm its effectiveness for the FASD.
Internal overvoltage accidents in the distribution network are likely to cause an equipment insul... more Internal overvoltage accidents in the distribution network are likely to cause an equipment insulation breakdown and result in system power outages and economic losses. Therefore, an internal overvoltage identification method based on the time-frequency atomic decomposition is investigated in this study. Firstly, the overvoltage waveforms are divided into four time periods. Then, the waveforms during these four time periods are decomposed by the atomic decomposition algorithm to obtain the effective atoms from the waveforms. Moreover, the root-mean-square (RMS) value of the zero-sequence voltage, the dominant atom frequency, the total relative matching degree, and the effective atom frequency are extracted as major features. In addition, the layered identification of the overvoltage types can be realized by combining the corresponding identification criteria. The salient advantage is the features with low dimension and a high degree of discrimination. Various overvoltage types can be identified just by the corresponding thresholds, and it is easier to deploy in the field than conventional methods based on classifier training. The effectiveness of the proposed method is verified by experimental results, and it concludes that the proposed algorithm has high accuracy and strong adaptability.
As key electrical equipment in the power system, the normal operation of a high-voltage circuit b... more As key electrical equipment in the power system, the normal operation of a high-voltage circuit breaker is related to the reliability and economy of the power supply. In this paper, a mechanical fault diagnostic method for a high-voltage circuit breaker via the hybrid feature extraction and the integrated extreme learning machine (IELM) is investigated. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain intrinsic mode functions (IMF). Then, the sub-band reconstruction of each order IMF component is performed by combining the Hilbert transform and the band-pass filter in order to obtain the time-frequency matrix. Moreover, mechanical fault feature vectors can be formed by the time-frequency entropy and the singular entropy, which are extracted by transforming the time-frequency matrix into the energy matrix and normalizing the frequency bands via the normal cumulative distribution function (NCDF). In addition, an IELM is built for the fault classification. The advantages of the proposed CEEMDAN scheme in combination with band-pass filtering can eliminate the modal aliasing, reduce the number of auxiliary noise additions, and improve the decomposition efficiency. Besides, the performance of the singular entropy normalized by the NCDF is more stable, and the IELM composed of multiple weak classifiers can solve the shortcomings of the traditional extreme learning machine. The experimental results based on measured data show the proposed method can effectively diagnose the mechanical failure via small samples of high-voltage circuit breakers. INDEX TERMS High-voltage circuit breaker, vibration signal, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), time-frequency entropy, singular entropy, integrated extreme learning machine (IELM).
Earth faults occur frequently in power distribution systems and they are usually accompanied by a... more Earth faults occur frequently in power distribution systems and they are usually accompanied by arc. This is a big hazard to the power distribution systems. Effective early detection is difficult to achieve using conventional methods, which brings inconvenience to fault location and maintenance. In order to solve this problem, a sensitive triggering algorithm should be equipped in the detection device. By analyzing the zero-sequence voltage under normal conditions and fault conditions, a discrete wavelet transform based triggering algorithm is proposed in this paper. A great number of waveforms simulated by the PSCAD/EMTDC software are used to test the algorithm and two other traditional algorithms, and the results are analyzed and compared. In addition, the curves recorded in the physical simulation systems and the field power distribution systems are input into the proposed algorithm to test its adaptability. For testing whether the algorithm could achieve real-time triggering, a device is designed to carry the algorithm program. These experiments show that the proposed algorithm has high reliability and it can meet the needs of real-time monitoring.
2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)
The critical step in the mechanical fault diagnoses of a high-voltage circuit breaker (HVCB) is t... more The critical step in the mechanical fault diagnoses of a high-voltage circuit breaker (HVCB) is the extraction of mechanical state characteristics. The effectiveness of these characteristics is related to the accuracy of the corresponding diagnostic results. A novel method for the mechanical fault feature extraction of a HVCB via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and weighted time-frequency entropy is proposed in this study. First, the time-frequency decomposition of vibration signals is performed by the CEEMDAN and the Hilbert transform to obtain the instantaneous frequency of intrinsic mode function (IMF) components. Then, the IMFs are reconstructed via a bandpass filter to gain a signal component of the specified frequency band. In addition, the weighted time-frequency entropy feature is extracted from the energy matrix. Through the verification of multiple groups of classifiers, the diagnostic results have a higher accuracy, which indicates that the proposed method can effectively characterize different mechanical faults.
2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE)
Data-driven fault diagnosis of high impedance fault (HIF) has received increasing attention and a... more Data-driven fault diagnosis of high impedance fault (HIF) has received increasing attention and achieved fruitful results. However, HIF data is difficult to obtain in engineering. Furthermore, there exists an imbalance between the fault data and non-fault data, making data-driven methods hard to detect HIFs reliably under the small imbalanced sample condition. To solve this problem, this paper proposes a novel HIF diagnosis method based on conditional Wasserstein generative adversarial network (WCGAN). By adversarial training, WCGAN can generate sufficient labeled zero-sequence current signals, which can expand the limited training set and achieve the balanced distribution of the samples. In addition, the Wasserstein distance was introduced to improve the loss function. Experimental results indicate that the proposed method can generate high-quality samples and achieve a high accuracy rate of fault detection in the case of small imbalanced samples.
Iet Generation Transmission & Distribution, Dec 1, 2019
A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault curre... more A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault current when a single-phase-to-ground fault occurred in distribution networks. However, passive ASDs cannot compensate for the active and harmonic components of ground-fault current. Furthermore, a massive residual ground-fault current would endanger human life and the reliable operation of distribution networks. To overcome the shortcomings of passive ASDs, a three-phase flexible ASD (FASD) with a three-phase cascaded H-bridge topology was presented. A three-phase FASD connected to a bus bar of distribution networks was employed to compensate for the ground-fault current. In this study, the FASD with the backstepping control (BSC) based on current-compensation principle was proposed to completely eliminate the ground-fault current. The effectiveness of the proposed FASD with the BSC method was verified using the simulation and experimental results.
In practice, the performance of distribution feeder parameter estimation is limited by the measur... more In practice, the performance of distribution feeder parameter estimation is limited by the measurement conditions in distribution networks. An accurate mathematical model that considers limited phasor measurements in distribution networks is necessary to estimate feeder parameters. This paper presents a set of modified parameter estimation models for unbalanced three-phase distribution feeders that only require the measurements of voltage amplitudes and power flows. To simplify the calculation process and improve the estimated results, a method combined with a radial basis function neural network (RBFNN) and multi-run optimization method (MRO), namely RBFNN-MRO, is proposed to calculate the parameters of distribution feeders. The relationship between the feeder parameters and the measurement data from the two terminals of the feeder can be mapped perfectly using the RBFNN. Furthermore, the random errors in the measurement device were eliminated using the proposed RBFNN-MRO algorithm. The RBFNN-MRO algorithm can limit the number of neurons in the hidden layer and substantially reduce the training time for each RBFNN. The feasibility of the proposed method was verified using four IEEE test systems. The proposed RBFNN-MRO and RBFNN methods were compared using the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN-MRO method has excellent potential for improving the accuracy of feeder parameter estimation even without synchronized phasor measurement. INDEX TERMS Parameter estimation method, radial basis function neural network (RBFNN), nonlinear relation, three-phase distribution feeder, maximum absolute percentage error (MAPE).
2018 China International Conference on Electricity Distribution (CICED), 2018
Peterson coil has not achieved excellent arc-suppression performance due to the increased harmoni... more Peterson coil has not achieved excellent arc-suppression performance due to the increased harmonic and active component of the fault current with the expansion and development of distribution networks. To address the problem, a flexible arc-suppression device (FASD) based on the three-phase cascaded H-bridge (CHB) converter has been proposed. This paper presents an interconnection strategy for the FASD in order to further improve the reliability of the device. When the FASD is under abnormal conditions such as line fault, incorrect fault-phase selection and excessive injection current, the proposed strategy achieves the interconnection of CHB converters between different phases to guarantee the uninterruptable operation of the arc-suppression function of the FASD. Simulation results are illustrated to verify the feasibility of the proposed interconnection strategy.
In recent years, many supervised learning algorithms have been successfully applied for photovolt... more In recent years, many supervised learning algorithms have been successfully applied for photovoltaic (PV) fault diagnosis. In practice, it is not possible to effectively obtain labels of large samples, limiting the engineering application of these algorithms. As for the unsupervised learning algorithm, it is completely adaptive learning, requiring a large number of samples to better learn the potential features in the data. To address the above problems, an improved online fault diagnosis method is proposed, which uses a small number of labeled samples to train the semisupervised ladder network (SSLN) fault diagnosis model to realize the diagnosis of line-to-line faults, open-circuit faults, partial shadow faults, and hybrid faults. In the proposed method, only the real-time operating voltage and current of PV array are needed for fault diagnosis. The sequential voltage and current of the PV array are first normalized, and the sequential power waveforms are obtained through numerical calculation. Then, the SSLN is used to extract the fault features from the sequence power waveforms. Finally, the classification is realized using the SSLN's noiseless encoder. To eliminate overfitting and improve convergence, the activation function, optimizer, and loss function of the SSLN is studied and improved. Meanwhile, numerical simulations and measured data verify that the proposed method provides strong anti-interference, and the diagnostic accuracies of both exceed 98%. Comparative experiments show that the proposed method outperforms algorithms such as squared-loss mutual information regularization, semisupervised support vector machine, graph-based semisupervised learning, and semisupervised extreme learning machine.
2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017
It is still a focus of research to detect the faulty feeder timely and accurately in resonant gro... more It is still a focus of research to detect the faulty feeder timely and accurately in resonant grounding distribution systems. The conventional methods commonly use single faulty feeder detection methods, such as wavelet transform method, transient energy method, and the fifth harmonic current method, etc. However, their reliability is not satisfied due to the partial fault information is considered. A novel approach to identify the faulty feeder based on discrete wavelet packet transform (DWPT) and machine learning is proposed in this paper. The time-frequency matrices are obtained by utilizing the DWPT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The feature vectors will be extracted manually by calculating time-frequency matrices with statistical quantities. The two classifiers (Adaboost+CART and SVM) are trained by a large number of feature vectors under various kinds of fault conditions and factors, respectively. The faulty feeder detection can be achieved by the trained two classifiers. A PSCAD/EMTDC simulator is established to simulate a practical 10-kV resonant grounding distribution system. Simulation results of the testing cases validate that the proposed approach of fault detection is able to achieve good identification accuracy.
2021 6th International Conference on Power and Renewable Energy (ICPRE), 2021
The treatment of the neutral point is essential for the distribution networks. The resonant groun... more The treatment of the neutral point is essential for the distribution networks. The resonant grounding system (RGS) is prone to generate the neutral point overvoltage caused by the low reactance of the arc suppression coil (ASC), which harms the operation of equipment and personal safety. The traditional passive neutral point overvoltage suppression strategies have poor adaptability, which do not meet the demands of increasing complexity of the distribution network lines. Therefore, we present a neutral point overvoltage suppression method based on backstepping control (BSC) with DC suppression. The BSC that prevents perturbation of system parameters achieves the neutral point overvoltage suppression. A DC suppression loop is constructed to realize the injection current DC suppression of the cascaded H-bridge (CHB) inverter, which is directly connected to the neutral point of the distribution networks. Simulation results verifies the effectiveness of neutral point overvoltage suppression based on BSC and the feasibility of DC suppression of injection current.
IEEE Transactions on Instrumentation and Measurement, 2021
Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in d... more Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order.
IEEE Transactions on Instrumentation and Measurement, 2021
In general, vibration signals generated by the switching operation of a high-voltage circuit brea... more In general, vibration signals generated by the switching operation of a high-voltage circuit breaker (HVCB) contains important information to reflect its mechanical status. A method for mechanical fault diagnoses of an HVCB based on a semisupervised stacked autoencoder (SSAE) and an integrated extreme learning machine (IELM) is proposed in this study. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain the time–frequency energy matrix. Then, an SSAE model is applied to automatically extract the characteristic information from the energy matrix. As a result, two-level classifiers can be constructed. The first level is utilized to identify normal or abnormal states, and the second level is selected to identify various types of faults in the abnormal state. The classifiers of these levels are composed of binary IELM. The advantages of the proposed method are that it not only can automatically extract the high-recognition features from the time–frequency energy matrix of high dimension to complete the identification of the existing fault types in the training set but also can accurately identify the samples of unknown types of faults. Experimental results show that the proposed method can effectively diagnose mechanical faults of an HVCB, and the classification accuracy reaches 99.5%.
IET Generation, Transmission & Distribution, 2019
A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault curre... more A Petersen coil was used as a passive arc-suppression device (ASD) to mitigate ground-fault current when a single-phase-to-ground fault occurred in distribution networks. However, passive ASDs cannot compensate for the active and harmonic components of ground-fault current. Furthermore, a massive residual ground-fault current would endanger human life and the reliable operation of distribution networks. To overcome the shortcomings of passive ASDs, a three-phase flexible ASD (FASD) with a three-phase cascaded H-bridge topology was presented. A three-phase FASD connected to a bus bar of distribution networks was employed to compensate for the ground-fault current. In this study, the FASD with the backstepping control (BSC) based on current-compensation principle was proposed to completely eliminate the ground-fault current. The effectiveness of the proposed FASD with the BSC method was verified using the simulation and experimental results.
International Journal of Electrical Power & Energy Systems, 2020
Because of the hazard of single-phase-to-ground faults on distribution networks for human securit... more Because of the hazard of single-phase-to-ground faults on distribution networks for human security and device insulation, it is important for the ground-fault current to be eliminated by an arc-suppression device. However, owing to their shortcomings, passive arc-suppression devices cannot compensate the ground-fault current fully. Therefore, active arc-suppression devices have been studied. The flexible arc-suppression device (FASD) with a cascaded H-bridge (HB) topology is one type of active arc-suppression device. An FASD connected to a bus bar of distribution networks is applied to mitigate the ground-fault current. Owing to the high voltage and large current during the ground fault, a large number of HBs are required for the FASD. To modulate the HBs, an improved distributed commutations modulation method, which is combined with the flexible arc-suppression method, is proposed. The proposed modulation strategy aims to minimise the error between the reference signal and the tracking signal during each sampling period. Finally, the proposed strategy is validated via simulations and experiments to confirm its effectiveness for the FASD.
Internal overvoltage accidents in the distribution network are likely to cause an equipment insul... more Internal overvoltage accidents in the distribution network are likely to cause an equipment insulation breakdown and result in system power outages and economic losses. Therefore, an internal overvoltage identification method based on the time-frequency atomic decomposition is investigated in this study. Firstly, the overvoltage waveforms are divided into four time periods. Then, the waveforms during these four time periods are decomposed by the atomic decomposition algorithm to obtain the effective atoms from the waveforms. Moreover, the root-mean-square (RMS) value of the zero-sequence voltage, the dominant atom frequency, the total relative matching degree, and the effective atom frequency are extracted as major features. In addition, the layered identification of the overvoltage types can be realized by combining the corresponding identification criteria. The salient advantage is the features with low dimension and a high degree of discrimination. Various overvoltage types can be identified just by the corresponding thresholds, and it is easier to deploy in the field than conventional methods based on classifier training. The effectiveness of the proposed method is verified by experimental results, and it concludes that the proposed algorithm has high accuracy and strong adaptability.
As key electrical equipment in the power system, the normal operation of a high-voltage circuit b... more As key electrical equipment in the power system, the normal operation of a high-voltage circuit breaker is related to the reliability and economy of the power supply. In this paper, a mechanical fault diagnostic method for a high-voltage circuit breaker via the hybrid feature extraction and the integrated extreme learning machine (IELM) is investigated. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain intrinsic mode functions (IMF). Then, the sub-band reconstruction of each order IMF component is performed by combining the Hilbert transform and the band-pass filter in order to obtain the time-frequency matrix. Moreover, mechanical fault feature vectors can be formed by the time-frequency entropy and the singular entropy, which are extracted by transforming the time-frequency matrix into the energy matrix and normalizing the frequency bands via the normal cumulative distribution function (NCDF). In addition, an IELM is built for the fault classification. The advantages of the proposed CEEMDAN scheme in combination with band-pass filtering can eliminate the modal aliasing, reduce the number of auxiliary noise additions, and improve the decomposition efficiency. Besides, the performance of the singular entropy normalized by the NCDF is more stable, and the IELM composed of multiple weak classifiers can solve the shortcomings of the traditional extreme learning machine. The experimental results based on measured data show the proposed method can effectively diagnose the mechanical failure via small samples of high-voltage circuit breakers. INDEX TERMS High-voltage circuit breaker, vibration signal, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), time-frequency entropy, singular entropy, integrated extreme learning machine (IELM).
Earth faults occur frequently in power distribution systems and they are usually accompanied by a... more Earth faults occur frequently in power distribution systems and they are usually accompanied by arc. This is a big hazard to the power distribution systems. Effective early detection is difficult to achieve using conventional methods, which brings inconvenience to fault location and maintenance. In order to solve this problem, a sensitive triggering algorithm should be equipped in the detection device. By analyzing the zero-sequence voltage under normal conditions and fault conditions, a discrete wavelet transform based triggering algorithm is proposed in this paper. A great number of waveforms simulated by the PSCAD/EMTDC software are used to test the algorithm and two other traditional algorithms, and the results are analyzed and compared. In addition, the curves recorded in the physical simulation systems and the field power distribution systems are input into the proposed algorithm to test its adaptability. For testing whether the algorithm could achieve real-time triggering, a device is designed to carry the algorithm program. These experiments show that the proposed algorithm has high reliability and it can meet the needs of real-time monitoring.
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