首页|Data-driven Fault Detection of Multiple Open-circuit Faults for MMC Systems Based on Long Short-term Memory Networks

Data-driven Fault Detection of Multiple Open-circuit Faults for MMC Systems Based on Long Short-term Memory Networks

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This paper presents a long short-term memory(LSTM)-based fault detection method to detect the multiple open-circuit switch faults of modular multilevel converter(MMC)systems with full-bridge sub-modules(FB-SMs).Eighteen sensor signals of grid voltages,grid currents and capacitance voltages of MMC for single and multi-switch faults are collected as sampling data.The output signal characteristics of four types of single switch faults of FB-SM,as well as double switch faults in the same and different phases of MMC,are analyzed under the conditions of load variations and control command changes.A multi-layer LSTM network is devised to deeply extract the fault characteristics of MMC under different faults and operation conditions,and a Softmax layer detects the fault types.Simulation results have confirmed that the proposed LSTM-based method has better detection performance compared with three other methods:K-nearest neighbor(KNN),naive bayes(NB)and recurrent neural network(RNN).In addition,it is highly robust to model uncertainties and Gaussian noise.The validity of the proposed method is further demonstrated by experiment studies conducted on a hardware-in-the-loop(HIL)testing platform.

Fault detectionlong short-term memory(LSTM)modular multilevel converter(MMC)open circuit fault

Chenxi Fan、Kaishun Xiahou、Lei Wang、Q.H.Wu

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School of Electric Power Engineering,South China University of Technology,Guangzhou 510641,China

Guangdong Basic and Applied Basic Research FoundationNational Natural Science Foundation of ChinaYoung Elite Scientists Sponsorship Program by CSEE

2020A151511110052207106CSEE-YESS-2022019

2024

10.17775/CSEEJPES.2022.05990

中国电机工程学会电力与能源系统学报(英文版)
中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI
ISSN：2096-0042
年,卷(期)：2024.10(4)