Adversarially Robust Fault Zone Prediction in Smart Grids With Bayesian Neural Networks

Abstract

The rapid growth of the global population, economy, and urbanization is significantly increasing energy consumption, necessitating the integration of renewable energy sources. This integration presents challenges that demand innovative solutions to maintain grid stability and efficiency. Smart grids offer enhanced reliability, efficiency, sustainability, and bi-directional communication. However, the reliance on advanced technologies in smart grids introduces vulnerabilities, particularly concerning adversarial attacks. This paper addresses two critical issues in smart grid fault prediction: the vulnerability of machine learning models to adversarial attacks and the operational challenges posed by false alarms. We propose a Bayesian Neural Network (BNN) framework for fault zone prediction that quantifies uncertainty in predictions, enhancing robustness and reducing false alarms. Our BNN model achieves up to 0.958 accuracy and 0.960 precision in fault zone prediction. To counter adversarial attacks, we developed an uncertainty-based detection scheme that leverages prediction uncertainty. This framework distinguishes between normal and adversarial data using predictive entropy and mutual information as metrics. It detects complex white-box adversarial attacks, which are challenging due to attackers’ detailed knowledge of the model, with a mean accuracy of 0.891 using predictive entropy and 0.981 using mutual information. The model’s performance, combined with minimal computational overhead, underscores its practicality and robustness for enhancing smart grid security.

Keywords

• Adversarial attacks
• Bayesian neural networks
• fault prediction
• smart grids
• uncertainty quantification