基于长短期记忆网络的 MMC 系统多重开路故障数据驱动型故障检测

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS
Chenxi Fan;Kaishun Xiahou;Lei Wang;Q. H. Wu
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引用次数: 0

摘要

本文提出了一种基于长短期记忆(LSTM)的故障检测方法,用于检测带有全桥子模块(FB-SM)的模块化多电平转换器(MMC)系统的多个开路开关故障。采集了单开关和多开关故障时 MMC 的电网电压、电网电流和电容电压等 18 个传感器信号作为采样数据。在负荷变化和控制指令变化的条件下,分析了 FB-SM 四种单开关故障以及 MMC 同相和异相双开关故障的输出信号特征。设计了多层 LSTM 网络来深度提取 MMC 在不同故障和运行条件下的故障特征,并由 Softmax 层检测故障类型。仿真结果证实,与其他三种方法相比,基于 LSTM 的方法具有更好的检测性能:与 K 近邻法(KNN)、天真贝叶斯法(NB)和递归神经网络法(RNN)相比,所提出的基于 LSTM 的方法具有更好的检测性能。此外,该方法对模型不确定性和高斯噪声具有很强的鲁棒性。在硬件在环(HIL)测试平台上进行的实验研究进一步证明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Driven Fault Detection of Multiple Open-Circuit Faults for MMC Systems Based on Long Short-Term Memory Networks
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.
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来源期刊
CiteScore
11.80
自引率
12.70%
发文量
389
审稿时长
26 weeks
期刊介绍: The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.
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