False data injection attack detection in dynamic power grid: A recurrent neural network-based method - Zhang, Feiye; Yang, Qingyu (Frontiers in Energy Research, 2022)
The smart grid greatly facilitates the transmission of power and information by the integration of the precise measurement technology and efficient decision support system. However, deep integration of the cyber and physical information brings multiple challenges to the grid operation. The false data injection attack can directly interfere with the results of state estimation, which causes the grid regulator to make wrong decisions and thus brings huge threat to the stability and security of grid operation. To address that issue, we propose a detection approach against false data injection attack of dynamic state estimation. The Kalman filter is used to dynamically estimate the state value from IEEE standard bus systems. The Long Short-Term Memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify the presence of attacks. We evaluate the effectiveness of proposed detection approach in multiple IEEE standard bus systems. Simulation results demonstrate that proposed detection approach outperforms the benchmarks in improving the detection accuracy of malicious attacks.