Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

Abstract

Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS.