基于隐马尔可夫模型支持卷积神经网络的直流电容状态监测

Tyler McGrew, V. Sysoeva, Chi-Hao Cheng, Mark Scott
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引用次数: 2

摘要

为了提高系统的可靠性和降低维护成本,针对不同电力电子拓扑结构中的各种电气元件开发了非侵入性状态监测技术。由于直流链路电容器对电力电子转换器的成本、尺寸和故障率的巨大影响,因此直流链路电容器是这些状态监测系统特别关注的组件。本文提出了一种非侵入式在线状态监测系统,用于对三相逆变器中MPPF直流链路电容的健康状况进行估计。利用直流母线上的电流传感器(CT)采集电流测量值,采用时频图像分类的新型状态监测方法分析15-43 MHz左右的高频电磁干扰(EMI)含量。该系统使用连续小波变换(CWT)、卷积神经网络(CNN)和隐马尔可夫模型(HMM)将直流链路电容器健康分为五个阶段之一,准确率为99.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Condition Monitoring of DC-Link Capacitors Using Hidden Markov Model Supported-Convolutional Neural Network
Non-invasive condition monitoring techniques have been developed for various electrical components within different power electronic topologies in order to increase reliability and decrease maintenance costs for these systems. DC-link capacitors are a component of particular attention for these condition monitoring systems due to their outsized effect on cost, size, and failure rate for power electronic converters. A non-invasive, online condition monitoring system is proposed in this paper which estimates the health of the MPPF DC-link capacitor within a 3-phase inverter. Current measurements are collected using a current transducer (CT) on the DC-bus, and a novel condition monitoring method of time-frequency image classification is used to analyze high frequency electromagnetic interference (EMI) content around 15-43 MHz. The proposed system uses a continuous wavelet transform (CWT), convolutional neural network (CNN), and Hidden Markov Model (HMM) to classify DC-link capacitor health into one of five stages with 99.9% accuracy.
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