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The work proposes the application of neural networks with particle swarmoptimisation (PSO) and genetic algorithms (GA) to compensate for missing datain classifying high voltage bushings. The classification is done using DGA datafrom 60966 bushings based on IEEEc57.104, IEC599 and IEEE production ratesmethods for oil impregnated paper (OIP) bushings. PSO and GA were compared interms of accuracy and computational efficiency. Both GA and PSO simulationswere able to estimate missing data values to an average 95% accuracy when onlyone variable was missing. However PSO rapidly deteriorated to 66% accuracy withtwo variables missing simultaneously, compared to 84% for GA. The dataestimated using GA was found to classify the conditions of bushings than thePSO.

Condition Monitoring of HV Bushings in the Presence of Missing Data Using Evolutionary Computing