volume | PIER Journals

Abstract

This paper introduces an intelligent Maximum Power Point Tracking (MPPT) framework for photovoltaic systems that achieves significant performance gains through two primary innovations: a dynamic search space optimization that intelligently constrains the search region to approximately 2% of the conventional area, and a sophisticated Q-learning algorithm operating within this optimized region. The framework establishes a real-time relationship between environmental conditions and maximum power point parameters for this aggressive search space reduction. For complex partial shading conditions, an adaptive switching mechanism dynamically activates an enhanced meta-heuristic optimization component with improved convergence properties, ensuring appropriate algorithm selection based on detected operating conditions. Experimental results demonstrate that under uniform irradiance, the framework achieves 99.12% tracking efficiency (a 3.34% improvement over P&O). Under rapidly changing conditions, it maintains 97.83% efficiency (compared to P&O's 90.12%), and under partial shading, it achieves 95.89% global MPPT efficiency (versus 76.25% for P&O). The proposed method significantly reduces steady-state oscillations to 0.41% (from 1.87% for P&O) and offers 42.3% faster convergence. While requiring moderately higher computational resources, the approach is implementable on medium-range microcontrollers, balancing performance with practical deployment.

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Ms. Xiaoping Lei