Load-current demand forecasting in substations of urban railway lines

Abstract

Load-current forecasting is important in the operation and energy management of urban railway lines. It helps control strategies to manage and distribute energy optimally, thereby saving energy and reducing voltage fluctuations. This paper presents a method to predict the traction current at the busbar of a substation using the supervised machine learning algorithm. Because the traction power load is supplied from both adjacent traction power stations, and the energy exchange process between trains also takes place during work, the input data are selected to combine the value history of busbar currents and feeder currents. Besides, the neural network configuration and the number of training cycles in the estimated model can be adjusted to achieve the desired accuracy. The proposed method was tested and adjusted based on the actual operation data at the Lang traction station on 24 June, 2022. The estimated results compared with measurement data from the supervisory control and data acquisition (SCADA) have proven that the largest absolute error is no more than 5 (A). The maximum relative error is not more than 0.005, and the mean squared error does not exceed 0.01 over the whole operating time of a day from 4h45 to 22h45.