Prediction Model for the Distribution Transformer Failure Using Correlation of Weather Data
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abstract
Distribution Transformer (DT) is an integral component of a distribution network. Electric utilities have invested interest in reducing DTs failure rates. This paper presents a method for prediction of probability of DT failure by analyzing a correlation between weather data and historical DT failure data. Logistic regression prediction model is used in order to predict DT failure, and to extract the correlation between weather parameters and DT failure rates. Accuracy of prediction is reliable, which is presented using evaluation metrics. This method not only has a vital significance for the maintenance of DTs, but also improves the economic efficiency and reliability of distribution network operation.
