An Early Stage Failure Prediction Mechanism in Smart Grid Networks

Smart grid systems, as modern cyber‐physical systems (CPS), introduce new interdependencies between power and communication components that can create new security challenges. One potential challenge that may arise is cascading failures resulting from cyber‐attacks or the failure of a component that needs to be detected in a timely manner. In this paper, we propose a novel early‐stage failure prediction (ESFP) mechanism that applies machine learning (ML) algorithms to enhance the security of smart grid systems. We use a realistic model to generate a dataset for training ML algorithms and develop a mechanism to predict the state of a system's components in the early stages before failures propagate in the system. ESFP can predict the final state of each power system component with respect to its initial failures. We apply the extreme gradient boosting (XGBoost) algorithm and examine the features of both the communication and power networks that provide high accuracy in predicting failures. We develop a new data generation procedure to construct a dataset containing electrical and network features and characteristics for training ML algorithms. ESFP also identifies the location of the initial failures as this allows for further protection plans and decisions. We evaluate the effectiveness of the proposed mechanism through an analysis conducted on an IEEE 118‐bus system. The proposed mechanism achieves 99.4% prediction accuracy in random attacks using the XGBoost algorithm. We also improve the time of the XGBoost algorithm by 75% by combining an unsupervised ML algorithm with this algorithm.