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Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines

机译：人工智能在永磁同步电机定子绕组故障诊断中的应用

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摘要

This paper proposes a new methodology to solve the problem of fault diagnosis in electrical machines. The fault diagnosis method presented in this paper is, first, able to provide information about the location of a short-circuit fault in a stator winding. Secondly, the method enables the estimation of fault severity by specifying the number of short-circuited turns during a fault. A cluster of Focused Time-Lagged neural networks are combined with the Particle Swarm Optimization algorithm for proposed fault diagnosis method.This method is applied to the stator windings of a Permanent Magnet Synchronous Machine. Each neural network, in the cluster, is trained to correlate the zero-current component to the number of short-circuited turns in the stator windings. The zero-current component, different from the zero-sequence current, are obtained by summing the instantaneous values of current on all phases of the stator winding during the diagnosis procedure. The neural networks are trained offline with the Extended Kalman Filter method using fault data from both computer simulations and an actual Permanent Magnet Synchronous Machine. The use of the Extended Kalman Filter method, for training, ensures that the neural network cluster used can be re-trained online to make the fault diagnosis system adapt to changing operational conditions. Results from both computer simulation and actual machine data are presented to show the performance of the neural network cluster and the Particle Swarm Optimization algorithm.
机译：本文提出了一种解决电机故障诊断的新方法。本文提出的故障诊断方法首先能够提供有关定子绕组中短路故障位置的信息。其次，该方法通过指定故障期间的短路匝数来估计故障的严重程度。结合时滞神经网络聚类和粒子群算法，提出一种故障诊断方法，该方法适用于永磁同步电机的定子绕组。训练群集中的每个神经网络，以使零电流分量与定子绕组中短路匝数相关。通过对诊断过程中定子绕组所有相上电流的瞬时值求和，可以得到不同于零序电流的零电流分量。使用扩展的卡尔曼滤波器方法，使用来自计算机仿真和实际永磁同步机的故障数据，对神经网络进行离线训练。使用扩展卡尔曼滤波方法进行训练，确保可以在线重新训练所使用的神经网络群集，以使故障诊断系统适应不断变化的运行条件。给出了计算机仿真和实际机器数据的结果，以显示神经网络群集和粒子群优化算法的性能。

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