  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.
