Condition Monitoring of Transformer Bushings Using Computational Intelligence

TL;DR

This paper explores the use of various artificial intelligence techniques, including novel methods like Rough Neural Network, to improve the condition monitoring of transformer bushings through optimized analysis of Dissolved Gas-in-oil data.

Contribution

It investigates gas relationships in DGA, applies multiple attribute reduction methods, and introduces a new classifier for high-dimensional noisy data.

Findings

Reduced attribute datasets improve classifier performance.

Rough Neural Network effectively handles high-dimensional noisy data.

Attribute reduction methods enhance decision accuracy.

Abstract

Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. There are different techniques used in determining the conditions from the data collected, but in this work the Artificial Intelligence techniques are investigated. This work investigates which gases in DGA are related to each other and which ones are important for making decisions. When the related and crucial gases are determined, the other gases are discarded thereby reducing the number of attributes in DGA. Hence a further investigation is done to see how these new datasets influence the performance of the classifiers used to classify the DGA of full attributes. The classifiers used in these experiments were Backpropagation Neural Networks (BPNN) and Support Vector Machines (SVM) whereas the Principal Component Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and Decision Trees (DT) were used to reduce the attributes of the dataset. The parameters used when training the BPNN and SVM classifiers are kept fixed to create a controlled test environment when investigating the effects of reducing the number of gases. This work further introduced a new classifier that can handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).