Explainable AI and Evolutionary Green Power Optimization for Secure Signal Processing in SGs with Privacy-Preserving Traffic Signal Control

The enormous amount of information generated by intelligent sensors and the need for rapid decision-making present significant challenges in signal processing, communication security, and system transparency. Existing AI-powered energy management platforms often function as “black boxes,” limiting interpretability and raising concerns related to data confidentiality, trust, and cyber-physical security. To address these issues, this study proposes an explainable artificial intelligence (XAI) and evolutionary green power optimization (EGPO) enabled secure signal processing framework for smart renewable energy grids with a particular emphasis on preserving data privacy in traffic signal regulation. The proposed approach integrates real-time signal analysis for fault detection, power estimation, and traffic-aware energy distribution with interpretable ML algorithms, including SHAP-enhanced deep neural networks and explainable reinforcement learning. Confidentiality is ensured through the use of distributed neural networks and homomorphic encryption (HE) while secure communication protocols and blockchain-based identity management enhance data integrity and security. Evolutionary optimization techniques are employed to improve energy allocation based on traffic conditions and to dynamically balance renewable energy loads. Experimental results demonstrate a significant reduction in communication delays and security threats, a 23% improvement in energy efficiency, and a 96.4% accuracy in anomaly detection, based on benchmark smart grid (SG) datasets and simulated traffic scenarios. This framework offers a highly intelligent, secure, and transparent foundation for next-generation power grids integrated with transportation networks.