Performance Enhancement of Renewable Energy System using Artificial Intelligence Control
DOI:
https://doi.org/10.70454/JRIST.020201Keywords:
renewable energy systems, artificial intelligence, intelligent control, photovoltaic systems, wind energy systems, hybrid renewable systems, predictive control, adaptive control, energy efficiency, power qualityAbstract
Renewable energy systems are increasingly essential to sustainable power generation because they reduce dependence on fossil fuels and support low-carbon energy transition. However, the performance of solar, wind, and hybrid renewable systems is often limited by intermittency, nonlinear operating characteristics, environmental uncertainty, and integration challenges. These factors reduce energy extraction efficiency, degrade power quality, and complicate stable system operation under varying load and resource conditions. To address these limitations, this article proposes an artificial-intelligence-based control framework for renewable energy systems with emphasis on performance enhancement through adaptive supervision, predictive regulation, and multi-objective decision-making. The study presents a structured review of renewable energy system characteristics, major artificial intelligence techniques for control, and the need for intelligent supervisory operation in modern energy environments. A novel control strategy, termed the Predictive Adaptive Multi-Objective Energy Regulator (PAMER), is introduced to improve efficiency, power quality, stability, and reliability in solar, wind, and hybrid renewable configurations. The framework combines sensor-based monitoring, data preprocessing, intelligent state estimation, adaptive control prioritization, and constraint-aware energy management within a closed-loop architecture. A performance enhancement methodology is then developed to evaluate system behavior using indicators such as efficiency, settling time, harmonic distortion, voltage deviation, and reliability-related constraints. Illustrative comparative analysis shows that the proposed AI-based framework can provide higher energy utilization, faster dynamic response, reduced overshoot, lower total harmonic distortion, and improved voltage regulation compared with conventional control approaches. The results support the view that artificial intelligence can significantly strengthen renewable energy system control by enabling more adaptive and resilient operation under fluctuating conditions. The article also discusses computational, data-related, and implementation challenges, and highlights future research opportunities in real-time deployment, explainable intelligent control, and advanced hybrid optimization for next-generation renewable energy systems.
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