Abstract
Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.
Highlights
Power transformers are among the most critical and expensive assets within the electrical transmission and distribution networks
Power transformers need to be continousely monitored during their entire operational life to avoid sudden and catstrophic failures
Over the past two decades, several condition monitoring techniques have been developed for power transformers [1]
Summary
Power transformers are among the most critical and expensive assets within the electrical transmission and distribution networks. With the continous increase in the load demand, power transformers are operating close to their nominal ratings and becoming more prone to failures. Power transformers need to be continousely monitored during their entire operational life to avoid sudden and catstrophic failures. Over the past two decades, several condition monitoring techniques have been developed for power transformers [1]. Among these techniques, insulation system has been a common key component to identify transformer health state and estimate its useful rmnant life [2]. The health condition and remnant life of power transformers have been assessed through various parameters of the insulation system [8], [9]
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