Robust False Data Injection Identification Framework for Power Systems Using Explainable Deep Learning

Although digital changes in power systems have added more ways to monitor and control them, these changes have also led to new cyber-attack risks, mainly from False Data Injection (FDI) attacks. If this happens, the sensors and operations are compromised, which can lead to big problems, disruptions, failures and blackouts. In response to this challenge, this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory (Bi-LSTM) networks and employs explanatory methods from Artificial Intelligence (AI). Not only does the suggested architecture detect potential fraud with high accuracy, but it also makes its decisions transparent, enabling operators to take appropriate action. The method developed here utilizes model-free, interpretable tools to identify essential input elements, thereby making predictions more understandable and usable. Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing. Benchmark power system data confirms that the model functions correctly through detailed experiments. Experimental results showed that Bi-LSTM + Explainable AI (XAI) achieved an average accuracy of 94%, surpassing XGBoost (89%) and Bagging (84%), while ensuring explainability and a high level of robustness across various operating scenarios. By conducting an ablation study, we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability. Additionally, examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time. The research offers a practical and flexible approach for detecting FDI attacks, improving the security of cyber-physical systems, and facilitating the deployment of AI in energy infrastructure.