Dynamic Incipient Fault Forecasting for Power Transformers Using an LSTM Model
Lin Wang, Tim Littler, Xueqin Liu
Abstract
Dissolved gas analysis (DGA) is a traditional approach for power transformer fault diagnostics based on the measurement of gas contamination. Hydrocarbon gases generated and dissolved in transformer oil during operation can increase in density as fault conditions predominate. Critical determination of gas concentration changes and assessment trending of dissolved gases for fault prediction and prevention of transformer damage is essential. In this article, a dynamic fault prediction approach is proposed using a long short-term memory (LSTM) model with intelligent classification to determine the running state of a transformer for prediction and avoidance of potential transformer damage. In the article, the LSTM model processed DGA data collected from real on-site transformer field measurements and predicts future dissolved gas concentrations in time sequence. Four artificial intelligence (AI) diagnostic models [support vector machine (SVM), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbors (KNN), decision tree, and artificial neural network (ANN)] were rendered and used for comparative fault prediction assessment. By comparing experimental results from the different LSTM-based models, this article asserts that the LSTM-KNN model provides the highest and most reliable prediction accuracy for power transformers.