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In this study, a cost-effective and non-invasive detection strategy is proposed for three-phase line start permanent magnet synchronous motor (LSPMSM) under mixed eccentricity fault using motor current signature analysis. In this respect, theory of air gap magnetic field in eccentric LSPMSM is presented. The detection strategy is examined through modeling and experimental studies. Two-dimensional time stepping finite element method is employed to calculate the motor parameters. Effects of mechanical load and fault severity on eccentric LSPMSM are also investigated. Different fault-related components are scrutinized and the most effective features are identified for this new type of electrical motor. The results indicate that amplitudes of fault-related components at rotor frequency are precise for early detection of mixed eccentricity in LSPMSM. Finally, an efficient frequency pattern as well as detection criterion is specified. [ABSTRACT FROM AUTHOR]

This article presents a classification methodology based on probabilistic neural networks. To automatically select the training data and obtain the performance evaluation results, the “K-fold” cross-validation method is used. Then, the probabilistic neural network is compared with the feed-forward neural network and the radial basis function network. The goal is to propose a classifier that is capable of recognizing 11 classes of three-phase distribution system faults to solve the complex fault (three-phase short-circuit) classification problem for reducing the multiple-estimation problem to estimate the fault location in radial distribution systems. The data for the fault classifier is produced by DigSilent Power Factory, Integrated Power System Analysis Software on an IEEE 13-node test feeder. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the probabilistic neural network classifier. It is shown that the probabilistic neural network approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed model can successfully be used as an effective tool for solving complicated classification problems. [ABSTRACT FROM AUTHOR]