Electric Power ›› 2021, Vol. 54 ›› Issue (11): 133-143.DOI: 10.11930/j.issn.1004-9649.202106040

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Fault Location for Active Distribution Network Based on Improved LMD and Energy Relative Entropy

XU Yanchun1, ZHAO Caicai1, SUN Sihan1, MI Lu2   

  1. 1. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station (China Three Gorges University), Yichang 443002, China;
    2. Department of Electrical and Computer Engineering, Texas A&M University College Station, College Station 77840, USA
  • Received:2021-06-23 Revised:2021-08-09 Online:2021-11-05 Published:2021-11-16
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No.51707102)

Abstract: In view of the characteristics of distribution network with large-scale integration of distributed generation when single-phase grounding fault occurs, such as complex structure and operation mode, obscure electrical quantity after fault, weak fault characteristics and so on, this paper proposes a new fault location method for active distribution network based on improved local mean decomposition (ILMD) and energy relative entropy. Firstly, the mirror extension is used to extend the signals at both ends to eliminate the endpoint effect of LMD. At the same time, the adaptive noise is added to the signal to overcome the mode aliasing problem of LMD, and the ILMD is used to decompose the transient zero sequence current of each section. Then the energy relative entropy of all product functions (PF) after decomposition is calculated. The sum of relative energy entropy of all PF components is the relative energy entropy between sections. The fault section is judged by the positioning criteria set in this paper. Simulation results show that the proposed algorithm has high accuracy in fault location for 10 kV small current grounding system and improved IEEE 33 node system under different simulation conditions, which verifies the accuracy of the proposed method.

Key words: active distribution network, fault location, local mean decomposition(LMD), mirror extension, adaptive noise, transient zero-sequence current