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

TL;DR

This paper explores neural network-based methods with evolutionary algorithms to accurately classify high voltage bushings despite missing data, comparing particle swarm optimization and genetic algorithms.

Contribution

It introduces a novel approach combining neural networks with PSO and GA for missing data compensation in bushing classification.

Findings

GA outperforms PSO in accuracy when multiple variables are missing.

Both methods achieve 95% accuracy with one missing variable.

GA provides more reliable data estimation for bushing condition classification.

Abstract

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.