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

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accuracy and computational efficiency. Both GA and PSO simulations were able to estimate missing data values to an average 95% accuracy when only one variable was missing. However PSO rapidly deteriorated to 66% accuracy with two variables missing simultaneously, compared to 84% for GA. The data estimated using GA was found to classify the conditions of bushings than the PSO. Key-Words: Bushings, DGA, Particle Swarm Optimisation (PSO), Genetic Algorithms, Autoencoder, Missing data, Regression