Journal
ISA TRANSACTIONS
Volume 39, Issue 4, Pages 433-439Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0019-0578(00)00031-8
Keywords
artificial intelligence; industrial computing; neural networks; induction motor; protection; SCADA
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This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults. (C) 2000 Elsevier Science Ltd. All rights reserved.
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