Abstract
Power transformer health index (PTHI) computation is performed based on the results of different tests, such as dissolved gas analysis (DGA), oil quality (OQ) evaluation, and depolarization factor (DP) testing. In this study, PTHI computation is performed using 631 dataset samples from Malaysia and 730 samples from the Gulf Region. A new model is proposed to predict the PTHI state by adopting intelligent classification methods (e.g., decision tree, support vector machine, k-nearest neighbor, and ensemble methods). The model is built via two-stage data processing. The first stage separates the test results into three modules that represent DGA, OQ, and DP factor codes. In the second stage, the output of the three modules is processed to predict the PTHI state. The four classification methods are applied to the proposed model, and the prediction accuracy of the PTHI state is determined. Results indicate that the proposed model has superior classification accuracy for each AI method compared with recent work. Furthermore, feature reductions are applied to minimize the testing time, effort, and costs. The reduced-feature models reveal the effectiveness of the adopted feature reduction technique. A slight difference in accuracy is observed between the full- and reduced-feature scenarios. Thus, the reduced-feature scenario is considered to decrease the effort and time of the computation process and the experimental cost. The proposed model is validated against uncertain noise in features of up to ±20%.
Highlights
The transformer is the most expensive equipment in the electric power system
A brief description of the four intelligent classification methods is presented in the appendix
The training samples (885) of the two sets were collected for training, and the testing samples (476) of the two sets were collected for testing
Summary
The transformer is the most expensive equipment in the electric power system. Monitoring the status of the insulation system in a power transformer is vital. Deterioration of the transformer insulating oil due to electrical and thermal stresses can lead to undesired transformer outage in electric power networks. The continuous evaluation of the power transformer state has elicited much attention. The condition of a transformer can be evaluated using a health index [1]. Establishing the power transformer health index (PTHI) is a challenge. It involves the fusion of present data from several sensors and important historical data. PTHI is a practical method that uses transformer test results to indicate the state of a power transformer
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