Application of machine learning in determining and resolving state estimation anomalies in power systems

Abstract

The state estimation (SE) process is one of the most important and efficient tools in achieving this goal. However, the occurrence of anomalies in the power grid, such as false data injection (FDI), can significantly impact the accuracy of the SE results. FDI results in reduced accuracy of SE results, potentially putting the power system in a critical situation. This paper addresses the limitations of simple residual-based algorithms in detecting FDI and proposes a scenario that utilizes machine learning (ML) models such as auto-encoder (AE), long short-term memory auto-encoder (LSTM AE), and 1-D convolutional neural network auto-encoder (1-D CNN AE) algorithms. The proposed method aims to offer a more effective FDI detection approach in the power grid, exhibiting higher detection accuracy. Also, the paper introduces the LSTM variational auto-encoder (LSTM VAE) algorithm for reconstructing anomalous data. By utilizing the LSTM VAE, anomalous data can be transformed to closely resemble the original data with an acceptable level of accuracy. Moreover, in critical situations involving FDI, the power system can maintain normal operation by employing the proposed method to reconstruct anomalous data. Finally, the performance of the presented methods is evaluated on the IEEE 14-bus, 30-bus, and 118-bus test networks. The results are then presented and discussed to demonstrate the effectiveness of the proposed method.