Artificial intelligence-driven dynamic optimization for predictive maintenance and cybersecurity in smart power distribution networks

The complexity of power distribution networks is rising, with smart technologies needed for efficient cybersecurity maintenance and risk management. Securing the scalability of predictive models across different grid configurations, managing massive amounts of sensor data, and integrating diverse data sources are all problems with existing approaches. Therefore, this study presents an artificial intelligence-based dynamic grid optimization analysis (AI-DGOA) method to address the above-discussed potential issues. By dynamically analyzing real-time data, AI-DGOA optimizes grid performance, allowing for proactive decision-making and lowering the risk of power supply disruptions. We design fault detection and classification algorithms based on machine learning ensemble models. The proposed algorithms improve predictive maintenance, reducing maintenance expenses and downtime by detecting impending equipment failures and grid inefficiencies. In addition, AI-DGOA ensures operational resilience and data security by incorporating real-time cybersecurity risk assessment to protect power distribution networks from changing cyber threats. The simulation analysis and results show that AI-DGOA effectively predicts grid disappointments, optimizes load distribution, and predicts cybersecurity vulnerabilities. This study reduces cyber risks, schedules maintenance more efficiently, and increases grid dependability. This study presents the role of Artificial Intelligence (AI) in revolutionizing power distribution networks through better cybersecurity risk management and predictive maintenance, leading to safer and more efficient power grids. Moreover, the extensive analysis shows that the proposed method outperforms other baseline methods, where secured power distribution is a key objective in the networks.