پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شمارهها | 865 |
| تعداد مقالات | 8,354 |
| تعداد مشاهده مقاله | 16,867,641 |
| تعداد دریافت فایل اصل مقاله | 11,288,565 |
A Chaotic Adaptive Quantum-Inspired GBMO Optimization for Precise Parameter Extraction of Photovoltaic Models | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 مهر 1405 اصل مقاله (825.25 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2026.55127.1756 | ||
| نویسنده | ||
| Elaheh Gharibshahian* | ||
| Department of basic science, Technical and Vocational University (TVU), Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Photovoltaic parameter estimation؛ quantum-inspired optimization؛ chaotic maps؛ adaptive control؛ double-diode model | ||
|
آمار تعداد مشاهده مقاله: 1 |
||