Wavelet based component fault detection in diode clamped multilevel inverter using probabilistic neural network

Monikuntala Bhattacharya, S. Saha, Dibyendu Khan, T. Nag
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引用次数: 6

Abstract

Multilevel inverter topology are becoming extremely important now-a-days due to the rapid growth in the renewable energy sector. Any kind of fault in the inverter directly hampers the normal operation of smart grid. In this context, this paper presents an intelligent technique for switch fault detection in diode clamped inverters for mid power application. It is necessary to detect and locate fault location and remove it quickly as reliability of power electronics components are important for stable operation of power system and electric drives. In this paper probabilistic neural network with discrete wavelet transform as signal preprocessor is employed for the fault detection in a three phase three level diode clamped inverter fed to LC load. Simulation results indicate highly satisfactory result with utmost of 99.8% fault detection accuracy.
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基于概率神经网络的二极管箝位多电平逆变器小波分量故障检测
由于可再生能源领域的快速发展,多电平逆变器拓扑结构变得极其重要。逆变器的任何一种故障都会直接影响智能电网的正常运行。在此背景下,本文提出了一种用于中功率二极管箝位逆变器开关故障的智能检测技术。电力电子元器件的可靠性对电力系统和电力传动的稳定运行至关重要,因此有必要对故障进行检测、定位和快速排除。本文将离散小波变换作为信号预处理的概率神经网络用于LC负载三相三电平二极管箝位逆变器的故障检测。仿真结果表明,该方法的故障检测准确率最高可达99.8%。
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