Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. (November 2022)

• Record Type: Journal Article
• Authors: Sharma, Abhishek; Sharma, Abhinav; Averbukh, Moshe; Rajput, Shailendra; Jately, Vibhu; Choudhury, Sushabhan; Azzopardi, Brian
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 6576
• Page End: 6592
• Publication Date: 2022-11
• Subjects: MFO -- OBLVMFO -- Lévy flight -- OBL -- Parameter optimization; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.05.011
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm's novelty lies primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation. Highlights: A novel OBLVMFO algorithm is proposed for parameter extraction of solar cell models. Two modifications are anticipated to overcome the limitations of original MFO algorithm. The performance of OBLVMFO is tested on standard benchmark functions and practical measured datasets. The proposed OBLVMFO algorithm is 61% faster than standard version of MFO algorithm.