ARTIFICIAL INTELLIGENCE APPLICATIONS FOR GRID-CONNECTED SOLAR INVERTERS

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications. This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, and grid synchronization. AI-driven MPPT algorithms significantly improve energy extraction efficiency, while deep learning-based fault detection methods enable early anomaly identification, reducing system failures and maintenance costs. Furthermore, fuzzy logic and RL enhance grid-inverter interactions, ensuring seamless synchronization and real-time adaptive control. AI-powered power quality management mitigates voltage fluctuations and harmonic distortions, maintaining compliance with grid standards. Despite the evident benefits, challenges such as computational complexity, real-time implementation constraints, and data availability hinder widespread adoption. This review provides an in-depth analysis of AI applications in grid-connected solar inverters, discussing existing solutions, challenges, and future research directions. Emphasis is placed on the potential of AI in advancing intelligent and resilient renewable energy systems through emerging technologies such as edge computing, blockchain, digital twins, and IoT-enabled smart grids.

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