A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

Abstract

The timely management of low voltage issues in the distribution network relies heavily on accurate predictions of voltage in the station areas. Many current methods for voltage prediction require complex data collection involving power grid topology parameters and electricity information. However, these methods suffer from drawbacks such as the need for extensive data, limited real-time performance, and significant prediction errors. Hence, this study proposes an approach to voltage prediction utilizing a combined LSTM-BP model. Initially, an analysis of the mechanism of low voltage formation in the station areas reveals that the primary factor influencing node points voltage is the power consumption of users. Subsequently, the LSTM neural network is employed to forecast short-term load curves in the distribution station areas, while the self-learning capability of the BP neural network is utilized to establish the correlation between users’ power and their corresponding voltage levels. By effectively combining the above two neural network models, the historical load’s data can be used to accurately and quickly predict the future low voltage situation in the station areas. Finally, taking station areas as the research object and comparing the actual voltage data with the voltage data predicted in this paper, the results show that within the 1V error range, the prediction accuracy is 99.8%. In contrast to the conventional voltage prediction approach, the method put forward in this research paper enables instantaneous and precise anticipation of low voltage levels without relying on details such as station configuration, line characteristics, or user voltage information.