Research On High Resistance Grounding Fault Identification Method In Distribution Network

Distribution network is the intermediate link connecting the power system and users.Its operation environment is complex and covers a wide area.It is easy to contact with the ground during operation,resulting in high resistance grounding fault.Due to the large grounding resistance of this type of fault,the transient steady-state electrical quantity does not change significantly when the fault occurs,and it is difficult for conventional protection to detect and operate reliably.Once the fault can not be eliminated in time,it may lead to electrical equipment damage and even fire,electric shock and other accidents.Aiming at this problem,this paper studies the extraction of fault characteristics and identification method of high-resistance grounding fault in distribution network.In this paper,the grounding mode of distribution network and transient steady-state electrical characteristics after single-phase grounding fault are summarized and analyzed,and the main characteristics of high resistance grounding fault are summarized.A 10 kV distribution network neutral grounding model via arc suppression coil is established by using PSCAD,based on this model,the specific parameters of the high-resistance grounding fault model were determined.The influences of different fault initial phase angles,fault positions and transition resistance values on the high-resistance fault zero-sequence current waveform characteristic values were simulated and analyzed.In order to solve the problem of weak zero-sequence current and easy to be affected by noise in high-resistance grounding fault,independent component analysis method is used in this paper to filter and denoise the zero-sequence current after fault.Firstly,the defects of the common denoising methods are analyzed,and the feature of blind source separation by independent component analysis is proposed to denoise the zero-sequence current;Then,the filtering effect of independent component analysis on different kinds of noise is verified,and the filtering effect of different denoising methods on zero-sequence current containing noise is compared;Finally,the stability of each denoising method is compared with signals with different signal-to-noise ratios,which proves that independent component analysis denoising has good noise filtering effect and stable performance.In view of the problems that it is difficult to extract the characteristic value of the zero-sequence current fault and identify the high-resistance grounding fault after the occurrence of high-resistance grounding fault,this paper uses wavelet transform to analyze the zero-sequence current waveform of two cycles after the occurrence of high-resistance grounding fault and other interference conditions.The wavelet approximate coefficient and the wavelet energy entropy of each feeder’s zero-sequence current are obtained as the characteristic quantity to form a fault data set,which is input to the improved Enniu algorithm optimized extreme learning machine for classification and recognition of high-resistance faults and other interference conditions,and the identification accuracy is compared with that of BP neural network and support vector machine.Finally,the anti-noise ability of the proposed high resistance identification method is tested and verified by a practical example.The results show that the proposed method has good anti-noise ability and practicability.There are 42 figures,12 tables and 89 references in this paper.