Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

Abstract

The increasing need to explore electric power grid expansion technologies like dynamic line rating (DLR) systems and their dependence on real-time weather data for system planning necessitates research into false data injection attacks (FDIA) on cyber-physical power systems (CPS). This study aims to develop a robust machine learning model to mitigate FDIA in DLR systems, focusing on statistical data processing, feature ranking, selection, training, validation and evaluation. It synthesises z-score and other statistical analyses with minimum redundancy maximum relevance (MR-MR) feature ranking and selection algorithm to improve model performance and generalisation of binary generalised linear model logistic regression (BGLM-LR) and other machine learning classification algorithms. The resulting models formed with BGLM-LR, Gaussian naïve Bayes (GNB), linear support vector machine (LSVM), wide neural network (WNN), and decision tree (DT) were trained and tested with 10-year hourly DLR history data features. The evaluation of the models on unseen data revealed enhanced validation and testing accuracies after the MR-MR feature ranking and selection. BGLM-LR, GNB, LSVM, and WNN showed promising performance for mitigating FDIA. However, the study identifies DT’s limitations as overfitting and lacking generalisation in FDIA mitigation. z-score-MR-MR-BGLM-LR and z-score-MR-MR-LSVM models exhibited outstanding performances with zero false negative rates highlighting the significance of feature ranking and selection. Still, the z-score-MR-MR-BGLM-LR combination exhibits the highest marginal improvement from training to testing, the lowest training and validation time and a perfect area under curve (AUC) of the receiver operating characteristics making it the best choice in mitigating FDIA when computational resources are limited.