基于多层神经网络的无刷同步发电机匝间短路故障检测

Pyae Phyo Tun, P. Kumar, Ryan Arya Pratama, Liu Shuyong
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引用次数: 5

摘要

定子绕组短路是电机中常见的故障之一。因此,为了避免在短时间内对机器造成灾难性故障,电气驱动系统的故障检测和消除对于安全关键应用是必要的。本文综述了近年来使用信号分析、基于模型的技术和人工智能机器诊断方法的故障检测和诊断技术。然后,对前馈神经网络进行训练,测试并验证该人工神经网络是否能够利用单位RMS 3相电流和电压量以及电流和电压的基次和三次谐波分量来分类健康和不同严重程度的匝间短路等级。
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Brushless Synchronous Generator Turn-to-Turn Short Circuit Fault Detection Using Multilayer Neural Network
Stator winding short circuit is one of the faults that occur frequently in electrical machines. Therefore, fault detection and elimination in electric drive systems is necessary for safety-critical applications in order not to cause catastrophic failure to the machine in a short time. This paper reviews recent fault detection and diagnosis techniques that use signal analysis, model-based techniques and artificial intelligence machine diagnosis methods. Then, feedforward neural network will be trained, tested and validated whether or not this artificial neural network can classified healthy and different severity inter-turn short circuit levels by using per unit RMS 3 phases current and voltage quantities as well as fundamental and third harmonic components of current and voltage.
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