Short circuit fault detection in permanent magnet synchronous motor based-on Group Model of Data Handling deep neural network

Abstract

Short circuit fault (SCF) in stator coils is one of the most common types of electrical faults. The expansion of this fault leads to the permanent demagnetization of the magnet and causes irreparable damage to the machine in a short period of time. With the development of artificial intelligence technologies and various machine learning and deep learning techniques, an increase in fault detection accuracy has been achieved. In this paper, permanent magnet synchronous motor (PMSM) was done in normal mode and types of fault (SCF in winding loops, phase to phase SCF, open circuit fault of one of the phases). Group Model of Data Handling deep neural network (GMDH-DNN) was used to produce a SCF detection model. By simulating the proposed method and on the data extracted from the PMSM, it was observed that the accuracy rate of SCF detection in the winding loops of the PMSM in the proposed method is equal to 99.2%, which is compared to other existing methods such as conditional generative adversarial network (CGAN). It has improved  1.7%. Also, by simulating other existing methods such as support vector machine (SVM), k nearest neighbors (KNN), C4.5, multi-layer perceptron (MLP), recursive deep neural network (RDNN), long short-term memory networks (LSTM), it was observed that the accuracy of the proposed method for SCFs detection in winding loops has been improved compared to all other methods.