Siamese Neural Network Architecture for Fault Detection in a Voltage Source Inverter

Lincoln Moura de Oliveira, Francisco Eduardo Mendes da Silva, Gabriel Marçal da Cunha Pereira, Demercil S. Oliveira, Fernando Luiz Marcelo Antunes
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引用次数: 3

Abstract

This study presents a methodology for evaluating the operating conditions of power converters through similarity analysis using the Siamesa Neural Network technique. The time series of the inverter waveforms are collected for 3 cycles, then converted and analyzed using image processing. For the evaluation of the method by simulation, initially the structure of the H bridge three-phase inverter is used, and later the ANPC hybrid topology with asymmetric modulation, both operating in open loop. The line voltage and current waveforms for the first and the line and phase voltage for the second are the parameters for defining and comparing normal steady-state operating conditions and open circuit conditions in the switches. The work presents the promising ability of siamese neural networks to observe the inverter operating conditions and carry out the identification and diagnosis of faults, given the prior knowledge of the circuit behavior, with the advantage of calculating the similarities even without the use of specific data bank for network training.
基于Siamese神经网络的电压源逆变器故障检测
本研究提出了一种利用Siamesa神经网络技术通过相似性分析来评估电源变换器运行条件的方法。采集3个周期的逆变器波形时间序列,然后进行图像处理和分析。首先采用H桥三相逆变器结构,然后采用非对称调制的ANPC混合拓扑结构,两种结构都是开环工作。第一种开关的线电压和电流波形以及第二种开关的线电压和相电压波形是定义和比较开关的正常稳态工作条件和开路条件的参数。研究表明,在给定电路行为的先验知识的情况下,连体神经网络具有观察逆变器运行状态并进行故障识别和诊断的良好能力,其优点是即使不使用特定的数据库进行网络训练也可以计算相似度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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