Evaluation of stochastic optimisation algorithms for induction machine winding fault identification

Abstract

This thesis is concerned with parameters identification and winding fault detection in induction motors using three different stochastic optimisation algorithms, namely genetic algorithm (GA), tabu search (TS) and simulated annealing (SA). Although induction motors are highly reliable, require low maintenance and have relatively high efficiency, they are subject to many electrical and mechanical types of faults. Undetected faults can lead to serious machine failures. Fault identification is, therefore, essential in order to detect and diagnose potential failures in electrical motors. Conventional methods of fault detection usually involve embedding sensors in the machines, but these are very expensive. The condition monitoring technique proposed in this thesis flags the presence of a winding fault and provides information about its nature and location by using an optimisation stochastic algorithm in conjunction with measured time domain voltage, stator current data and rotor speed data. This technique requires a mathematical ABCabc model of the three-phase induction motor. The performance of the three stochastic search methods is evaluated in this thesis for their use to identify open-circuit faults in the stator and rotor windings of a three-phase induction motor. The proposed fault detection technique is validated through the use of experimental data collected under steady-state operating conditions. Time domain terminal voltages and the rotor speed are used as input data for the induction motor model while the outputs are the calculated stator currents. These calculated currents are compared to the measured currents to produce a set of current errors that are integrated and summed to give an overall error function. Fault identification is achieved by adjusting the model parameters off-line using the stochastic search method to minimise this error function. The estimate values for the winding parameters give the best possible match between the performance of the faulty experimental machine and its mathematical ABCabc model. These estimates of the values of the motor winding parameters are used in the detection of the development of faults by identifying both the location and the nature of the winding fault. The effectiveness of the three stochastic methods to identify stator and rotor winding faults are compared in terms of the required computation resources and their success rates in converging to a solution.