A Method for Dissolved Gas Forecasting in Power Transformers Using LS-SVM

Maintenance data from power transformers are typically in the form of dissolved gas analysis time series data. This research attempts consolidating industry knowledge on the maintenance of power transformers and time series forecasting techniques into a coherent system for the purpose of predictive maintenance of power transformers. The generalisability of forecasting models is investigated by measuring performance of single models across multiple transformers, and hence, multiple data sets. A novel method of data preprocessing is utilized; an exponential smoothing technique specifically designed for the type of raw data received (aperiodically sampled, noisy data). In addition, industry specified features for fault classification from the literature were added to the data set. These other features were examined to see if they improved forecasting. The forecasting techniques evaluated included Least-Squares Support Vector Machine (LS-SVM) with hyper-parameters optimized using a Particle Swarm Optimisation; Support Vector Regressors; Naive forecasts; Mean forecasts; Auto-Regressive Integrated Moving average or ARIMA; and Exponential Smoothing. Two sets of experiments were run, which differed in how the Training, Validation, and Testing sets were chosen. These experiments were run for different input vectors; the original input vector and an input vector augmented with the industry specified features. Models trained in the second experiment outperformed all other models, when the distributions of the Testing errors were considered. When generalisability was considered, it was found that the models trained across all transformers outperformed the per-transformer models that treated the data as univariate time series.