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首页> 外文期刊>American journal of applied sciences >PARTICLE SWARM AND NEURAL NETWORK APPROACH FOR FAULT CLEARING OF MULTILEVEL INVERTERS
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PARTICLE SWARM AND NEURAL NETWORK APPROACH FOR FAULT CLEARING OF MULTILEVEL INVERTERS

机译：多级逆变器故障清除的粒子群算法和神经网络方法

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摘要

This study presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a Particle Swarm Optimization (PSO) based machine learning algorithm. A structured Particle Swarm (PS)-neural network system has been designed, developed and trained to detect and isolate the most common types of faults: single switch open circuit faults, post-short circuits, short circuits and the unknown faults. Extensive simulation experiments were conducted to test the system with added noise and the results show that the structured neural network system which was trained by using the proposed machine learning approach gives high accuracy in detecting whether a faulty condition has occurred, thus isolating and pin-pointing to the type of faulty conditions occurring in power electronics inverter based electrical drives. Finally, the authors show that the proposed structured PS-neural network system has the capability of real-time detection of any of the faulty conditions mentioned above within 20 milliseconds or less.
机译：这项研究提出了一种用于感应电动机驱动器故障诊断的机器学习技术。使用通用的商业仿真工具开发并实施了逆变器-电动机组合的正常模型和广泛的故障模型，以在基于粒子群优化（PSO）的机器选择的广泛工作点上生成电压和电流信号学习算法。已经设计，开发和培训了结构化的粒子群（PS）神经网络系统，以检测和隔离最常见的故障类型：单开关开路故障，短路后，短路和未知故障。进行了广泛的仿真实验以测试系统的添加噪声，结果表明，使用所提出的机器学习方法训练的结构化神经网络系统在检测是否发生故障情况方面具有很高的准确性，因此可以进行隔离和精确定位出现在基于功率电子逆变器的电气驱动器中的故障条件类型。最后，作者表明，所提出的结构化PS神经网络系统具有在20毫秒或更短时间内实时检测到上述任何故障情况的能力。

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