1 research outputs found
Condition Monitoring of Transformer Bushings Using Computational Intelligence
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)