Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System

Abstract

Fast and stochastic power fluctuations caused by renewable energy sources and flexible loads have significantly deteriorated the frequency performance of modern power systems. Power system frequency control aims to achieve real-time power balance between generations and loads. In practice, it is much more difficult to exactly acquire the values of unbalance power in both transmission and distribution systems, especially when there is a high penetration level of renewable energies. This paper explores a deep learning approach to identify active power fluctuations in real-time, which is based on a long short-term memory recurrent neural network. The developed method provides a more accurate and faster estimation of the value of power fluctuations from the real-time measured frequency signal. The identified power fluctuations can serve as control reference so that the system frequency can be better maintained by automatic generation control, as well as emerging frequency control elements, such as energy storage system. A detailed model of Singapore power system integrated with distributed energy storage systems is used to verify the proposed method and to compare with various classical methods. The simulation results clearly demonstrate the necessity for power fluctuation identification, and the advantages of the proposed method.