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
A single-qubit quantum classifier(SQC)based on a gradient-free optimization(GFO)algorithm,named the GFO-based SQC,is proposed to overcome the effects of barren plateaus caused by quantum devices.Here,a rotation gate R...A single-qubit quantum classifier(SQC)based on a gradient-free optimization(GFO)algorithm,named the GFO-based SQC,is proposed to overcome the effects of barren plateaus caused by quantum devices.Here,a rotation gate R_(X)(φ)is applied on the single-qubit binary quantum classifier,and the training data and parameters are loaded intoφin the form of vector multiplication.The cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum circuit.The algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop condition.The proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine(QSVM).Furthermore,the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise.The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time.Additionally,the proposed GFO algorithm can quickly complete the training process of the SQC.Importantly,the GFO-based SQC has a good performance in noisy environments.展开更多
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.
基金Project supported by the National Natural Science Foundation of China(Grant No.62375140)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX190900)。
文摘A single-qubit quantum classifier(SQC)based on a gradient-free optimization(GFO)algorithm,named the GFO-based SQC,is proposed to overcome the effects of barren plateaus caused by quantum devices.Here,a rotation gate R_(X)(φ)is applied on the single-qubit binary quantum classifier,and the training data and parameters are loaded intoφin the form of vector multiplication.The cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum circuit.The algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop condition.The proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine(QSVM).Furthermore,the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise.The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time.Additionally,the proposed GFO algorithm can quickly complete the training process of the SQC.Importantly,the GFO-based SQC has a good performance in noisy environments.
