Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning

New advances in technology have greatly improved the monitoring and controlling of power networks, but these advances leave the system open to cyber attacks. One common attack is known as a False Data Injection Attacks (FDIAs), which poses serious threats to the operation and control of power grids. Hence, recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to detect attacks. Inspired by these advancements, we have developed a new methodology for not only identifying AC FDIAs but, more importantly, for correction as well. Our methodology utilizes a Long-Short Term Memory Denoising Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the attacked measurements. The method was evaluated using the IEEE 30 system, and the experiments demonstrated that the proposed method was successfully able to identify the corrupted states and correct them with high accuracy.