Monitoring Transformer Condition with MLP Machine Learning Model

Abstract

Failures of large power transformers in transmission system are always followed by significant costs, which is especially problematic because they present an unplanned expenditure. Aside from derailing financial plans, these events can lead to lower system reliability. This paper describes the development and potential application of transformer model based on multilayer perceptron class of artificial neural networks. Model is built in Python programming language and data collected over the span of one year for a single transformer. Three input features (oil temperature, winding current and outside temperature) are used in the input layer, with the goal of predicting the winding temperature in the transformer. Predicted temperature of the windings can then be compared with the actual winding temperature, which can serve as an indicator of transformers internal condition. Two types of transformer condition degradation are simulated to show the model in action, and certain indicators are explored.