A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Yijin Li, Jianhua Lin, Geng Niu, Ming Wu and Xuteng Wei
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Yijin Li: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Jianhua Lin: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China
Geng Niu: State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China
Ming Wu: State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China
Xuteng Wei: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China

Energies, 2021, vol. 14, issue 16, 1-16

Abstract: Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

Keywords: fault detection; instantaneous phase difference of current high-frequency component (IPDCHC); Hilbert–Huang Transform (HHT); self-adaptive threshold; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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