Singla Manish Kumar, Gupta Jyoti, Parag Nijhawan, Ekta Thakur, Tella Teshome Goa, Mosaad Mohamed I, Murodbek Safaraliev
Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.
Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
Heliyon. 2024 Jul 2;10(13):e33952. doi: 10.1016/j.heliyon.2024.e33952. eCollection 2024 Jul 15.
The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.
随着太阳能部署的扩大，精确估计太阳能光伏电池参数变得越来越重要。由于太阳能光伏电池具有复杂和非线性的特性，元启发式算法在参数估计方面比传统算法更具潜力。本研究利用受雄性河豚圆形结构启发的河豚（PF）元启发式优化方法来估计改进型四二极管光伏电池的参数。针对十个基准测试函数评估了PF算法的性能，结果以均值和标准差表示以进行验证。与粒子群优化（PSO）、灰狼优化（GWO）、大鼠搜索算法（RAT）、基于堆的优化器（HBO）和布谷鸟搜索（CS）算法的对比分析突出了PF的优越性能，以7.8947E-08的最小误差实现了最优解。包括弗里德曼排名（第1）和威尔科克森秩和（3.8108E-07）在内的统计测试证实了PF的优越性。雄性河豚的圆形结构为优化算法提供了一个有效的模型，提高了参数估计能力。基准测试和统计分析一致强调PF优于其他元启发式算法。未来的研究应探索PF在太阳能及其他领域的潜在应用。
