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Fault Line Selection Method Of Small Current Grounding System Based On VMD-DTW Clustering

Posted on:2022-09-17Degree:MasterType:Thesis
Country:ChinaCandidate:J WangFull Text:PDF
GTID:2492306521955739Subject:Electrical engineering
Abstract/Summary:
At present,China’s low-and medium-voltage power distribution network generally adopts low-current grounding operation.The system using this method can continue to operate for a period of time after a single-phase grounding fault occurs,but in order to prevent the expansion of the fault area due to electrical equipment damage,or other factors The impact expands to phase-to-phase faults,and the faulty circuit needs to be found and dealt with in time.The frequency of single-phase grounding faults in small current grounding system is the largest,and the grounding current after the fault is extremely small,and there is no obvious fault feature.The existing fault line selection method is difficult to accurately identify the fault line.Therefore,this article has done the following research on the single-phase grounding fault line selection of the small current grounding system of the distribution network to realize the accurate selection of the fault line.First,the current research status of single-phase grounding fault line selection methods for small-current grounding systems at home and abroad is summarized,and the shortcomings of each method are explained;aiming at the scenario of a single-phase grounding fault in a small current grounding system,the characteristics of the normal line and the fault line are analyzed in detail from the two aspects of steady-state characteristics and transient characteristics;by comparing and choosing a suitable time-frequency analysis method,the fault characteristic quantity is extracted.At the same time,the single-phase grounding fault model of small current grounding system is built for simulation analysis to verify whether the steady-state and transient characteristics are consistent with the theoretical analysis,and the influence of fault initial phase angle,transition resistance and fault point position on zero sequence current is analyzed.Secondly,A method of fault line selection based on the combination of Dynamic Time Warping and improved Hierarchical Agglomerative Clustering is proposed.Based on the decomposition results of the fault characteristic quantities by the variational modal decomposition method,the Dynamic Time Warping method is used to measure the similarity between the transient zero sequence currents of any two lines.That is,the similarity between the transient zero sequence currents of any two lines is calculated based on the modal components obtained by the decomposition.On this basis,an improved Hierarchical Agglomerative Clustering method is used to cluster transient zero-sequence currents.The final clustering result contains a cluster with a small number of samples as the fault cluster,and the samples inside are the modal components of the transient zero-sequence current of the faulty line.Finally,build a model in MATLAB/Simulink for simulation analysis.The results of route selection in different scenarios are simulated,and the route selection effects of the method proposed in the article and several common methods under different influencing factors are compared.The final results show that: in different scenarios,the method proposed in the article can accurately select the faulty line;under noise interference,sampling asynchronous interference,and when the sampling frequency is different,among several methods for comparison,the method proposed in the article has the highest line selection accuracy.Especially when the initial phase angle of the fault is small and the sampling information is missing,the method proposed in the article can also be used to accurately select the line.The method proposed in the article makes up for the shortcomings of the existing route selection methods and plays a certain role in promoting the development of route selection methods.
Keywords/Search Tags:Single-phase ground fault, failure line selection, variational modal decomposition, dynamic time consolidation, hierarchical cohesion clustering
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