AbstractThis paper proposes a new method using Artificial Neural Network (ANN) for detection of different Inter Turn Short Circuit (ITSC) faults in an induction motor under different loading conditions. The stator current signal was obtained experimentally from a healthy motor and a faulty motor with ITSC faults. The statistical time domain features was extracted from stator current signal, these features are used to train and test an ANN in order to diagnose ITSC faults. A complete study is performed by considering various diagnosis methods from ANN and machine learning algorithms, including Decision Tree (DT), K-Nearest Neighbours (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) for diagnosis ITSC faults. The performance of the proposed method was compared with machine learning algorithms, the proposed method has a higher accuracy than the other algorithms. Trained neural networks are able to classify different states of the ITSC faults with satisfied accuracy. The efficiency of this approach has been proven using experimental tests to diagnose ITCS faults in a 1.5Hp squirrel cage induction motor.


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    Title :

    Detection of Inter Turn Short Circuit Faults in Induction Motor Using Artificial Neural Network




    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown