Document Type : Research Articles
Author
Department of basic science, Technical and Vocational University (TVU), Tehran, Iran
Abstract
This paper presents a novel Chaotic Adaptive Quantum-Inspired Gases Brownian Motion Optimization (CAQI-GBMO) algorithm for accurate parameter estimation of photovoltaic models. The proposed method addresses the fundamental challenge of balancing exploration and exploitation in metaheuristic optimization by integrating three complementary mechanisms: quantum behavior modeling enables particles to maintain population diversity through probabilistic position distributions; multiple chaotic maps enhance search space coverage with superior statistical properties; and adaptive parameter control dynamically adjusts exploration-exploitation balance throughout the optimization process. The algorithm is specifically designed for the double-diode model parameter estimation problem, which involves seven unknown parameters with strong correlations and a multimodal objective function landscape. Experimental validation using benchmark R.T.C. France solar cell data demonstrates that CAQI-GBMO achieves a minimum RMSE of 9.824708 × 10⁻⁴for the double-diode model, matching the best-known results with superior consistency across 5000 independent runs. Comparative analysis against state-of-the-art algorithms, including SENMSSA, iAPO, and IWSO, reveals that CAQI-GBMO achieves the lowest Friedman ranking of 1.62, indicating statistically significant performance improvements. The accurate parameter estimates enable precise reproduction of current-voltage and power-voltage characteristics, which are essential for maximum power point tracking, system simulation, and performance optimization in practical photovoltaic installations.
Keywords
- Photovoltaic parameter estimation
- quantum-inspired optimization
- chaotic maps
- adaptive control
- double-diode model
Main Subjects
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