A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array. (1st December 2020)

• Record Type: Journal Article
• Authors: Yousri, Dalia; Babu, Thanikanti Sudhakar; Mirjalili, Seyedali; Rajasekar, N.; Elaziz, Mohamed Abd
• Is Part Of: Energy conversion and management. Volume 225(2020)
• Publication Date: 2020-12-01
• Subjects: Artificial ecosystem-based optimization -- Particle Swarm Optimization -- Photovoltaic reconfiguration -- Large photovoltaic array -- Mitigation techniques -- Partial shading -- Optimization -- heuristic -- particle swarm optimizer; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113385
• Languages: English
• ISSNs: 0196-8904

Abstract

Abstract: Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 × 9, 6 × 20, 16 × 16, and 25 × 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization–reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization–photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs. Highlights: A novel fitness function is proposed for photovoltaic reconfiguration approach. Artificial ecosystem optimizer is applied to different patterns of shadow. The proposed approach is implemented for 9x9, 6x20, 16x16, and 25x25 photovoltaic arrays. The results of the proposed technique are compared with recent counterparts. Several metrics are computed to confirm the superiority of the proposed approach. Artificial ecosystem optimizer based on the novel objective boosts the produced power.