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Induction Motor Relaying Scheme for External Faults Detection and Classification Using Subtractive Clustering Based Sugeno Fuzzy Inference System

Abstract Conventional thermal relays generally suffer from inaccurate induction motor external faults identification. Recent developments in diagnosis systems have led to the scope of radically different diagnosis strategies based on artificial intelligence techniques. This study use fuzzy logic as a software tool and relay logic for external faults identification of an induction motor. In this study, a subtractive clustering based Sugeno-type fuzzy inference system is utilized to classify five external faults and the normal condition of a low-voltage three-phase induction motor using simulation and practical data sets. Five external faults- overload, over-voltage, under-voltage, single-phasing, and voltage unbalance-along with normal conditions are simulated on an induction motor over a wide range of operating voltage and load torque; sampled three-phase RMS voltages and currents are used as input feature vectors to train the fuzzy inference system. Average total classification accuracy and average root means square error obtained with ten-times random sub-sampling cross-validation of test data sets are used to find the best accurate and generalized fuzzy inference system configuration among different cluster radius fuzzy inference systems for both simulation and real-time data sets. This method accurately detects external faults with 94.37% average total classification accuracy for practical test data sets.