AI-Driven Neuromorphic System for Sustainable Marine Energy Integration and Power System Stability Enhancement

This work presents an innovative approach to integrating artificial intelligence (AI) and neuromorphic computing to address the critical challenges of power system stability and sustainable marine renewable energy deployment. We propose a novel AI-driven neuromorphic system that seamlessly combines adaptive control of marine energy installations, real-time power system stability analysis, and comprehensive environmental impact assessment capabilities. At the core of our system is a neuromorphic computing-assisted thermoelectric capacitive-coupled sensor array that provides high-fidelity environmental and power system data. This sensor network feeds into a deep reinforcement learning framework that optimizes marine renewable energy device performance while minimizing environmental impacts and enhancing grid stability. Our experimental results demonstrate significant improvements in power system stability metrics, with voltage profiles improving by 28% and frequency deviations reduced by up to 35% compared to conventional control methods. The environmental impact assessment component shows a 40% increase in accuracy for predicting potential ecological effects when compared to traditional assessment techniques. Furthermore, the adaptive control mechanism increases energy harvesting efficiency by 25% across variable marine conditions. This multidisciplinary approach effectively addresses the complex challenges of integrating sustainable marine energy into existing power systems by leveraging cutting-edge AI, neuromorphic computing architectures, and renewable energy engineering principles. The system's ability to simultaneously optimize energy production, ensure grid stability, and minimize environmental impacts represents a significant advancement in sustainable energy integration technologies with practical applications for marine renewable energy development globally.