Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to thei...Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to their high settings of the action value when a high-impedance grounding fault occurs.In this study,the relationship between the zero-sequence currents of each feeder and the neutral branch was analyzed.Then,a grounding protection method was proposed on the basis of the zero-sequence current ratio coefficient.It is defined as the ratio of the zero-sequence current of the feeder to that of the neutral branch.Nonetheless,both zero-sequence voltage and zero-sequence current are affected by the transition resistance,The influence of transition resistance can be eliminated by calculating this coefficient.Therefore,a method based on the zero-sequence current ratio coefficient was proposed considering the significant difference between the faulty feeder and healthy feeder.Furthermore,unbalanced current can be prevented by setting the starting current.PSCAD simulation results reveal that the proposed method shows high reliability and sensitivity when a high-resistance grounding fault occurs.展开更多
Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-ba...Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.展开更多
Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a fault...Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.展开更多
基金supported in part by National Key Research and Development Program of China(2016YFB0900603)Technology Projects of State Grid Corporation of China(52094017000W).
文摘Definite-time zero-sequence over-current protection is presently used in systems whose neutral point is grounded by a low resistance(low-resistance grounding systems).These systems frequently malfunction owing to their high settings of the action value when a high-impedance grounding fault occurs.In this study,the relationship between the zero-sequence currents of each feeder and the neutral branch was analyzed.Then,a grounding protection method was proposed on the basis of the zero-sequence current ratio coefficient.It is defined as the ratio of the zero-sequence current of the feeder to that of the neutral branch.Nonetheless,both zero-sequence voltage and zero-sequence current are affected by the transition resistance,The influence of transition resistance can be eliminated by calculating this coefficient.Therefore,a method based on the zero-sequence current ratio coefficient was proposed considering the significant difference between the faulty feeder and healthy feeder.Furthermore,unbalanced current can be prevented by setting the starting current.PSCAD simulation results reveal that the proposed method shows high reliability and sensitivity when a high-resistance grounding fault occurs.
基金the Key Program of the Chinese Academy of Sciences under Grant QYZDJ-SSW-JSC025in part by the National Natural Science Foundation of China under Grant 51721005,and in part by the Chinese Scholarship Council(CSC).
文摘Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.
文摘Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.
