Photovoltaic Array Reconfiguration System for Maximizing the Harvested Power Using Population-Based Algorithms

Abstract

Massive infiltration of photovoltaic (PV) systems into electric supply networks creates numerous challenges in the present era, as the PV systems become an alternative to non-renewable energy resources. Partial shading, nevertheless, is an essential problem which affects the productivity and life of PV plants. PV reconfiguration is known as a powerful technique to resolve this effect. It is achieved by rearranging the PV modules according to their temperature and levels of shade. Therefore, in this paper, we have utilized three simple population-based optimization algorithms that are known as the flow regime algorithm (FRA), the social mimic optimization algorithm (SMO), and the Rao optimization algorithm to dynamically restructure the PV array. The effectiveness of the proposed algorithms is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the results obtained are compared with a regular total-cross-tied (TCT) connection and recently published techniques such as the competence square (CS) and genetic algorithm (GA). Furthermore, to demonstrate the suitability of proposed approaches in real-time implementation, real-time irradiation data of a particular location are considered and fed into the proposed algorithms for effective shade dispersion. After successful shade dispersion, the total energy generated using the three proposed algorithms is calculated and compared with the TCT reconfigured system for one year. The presented energy calculations and revenue generation confirm that the power produced by the proposed FRA technique is 13% higher than that generated by the TCT configuration. Furthermore, the presented PV characteristics show a reduced number of multiple peaks in the system. Thus, the proposed FRA technique can be endorsed as a technique that is superior to other existing methods.

Impact of Partial Shading on Various PV Array Configurations and Different Modeling Approaches: A Comprehensive Review

“…Moreover, high shade dispersion property is introduced by using FRA, SMO algorithm, and RAO algorithm which helped to enhance power 13%, 11%, and 9% respectively as compared to 9×9 size of TCT, CS, GA configurations. Performance parameters such as FF, ML, %PL and %PE are observed and found higher power, reduced MPP for FRA under three non-uniform shading levels with 200W/m 2 , 400W/m 2 , 500W/m 2 , 600W/m 2 and 900W/m 2 [143].…”

“…In this paper, various types of PV array configuration models are reviewed and investigated in terms of their performance parameters such as accuracy, reliability, (BL-HC) 3 [118,126,136] RTCT 2 [100,118] [91] New configuration 1 [93] Novel configuration 1 [120] SDS configuration 1 [120] Latin square (LS) 1 [129] Dominance Square (DS) 2 [130,139] OE configuration 1 [133] Skyscraper 1 [134] Ladder configuration 1 [136] Competence Square (CS) 3 [139,143,147] Lo-Shu 1 [139] Metaheuristic-TCT based PV array configurations GA 5 [142][143][144][145]147] PSO (ASM based configuration)…”

“…3 [141,145,147] FRA 1 [143] SMO 1 [143] RAO 1 [143] GOA 1 [144] BOA 1 [146] MMHO 1 [147] In this present comprehensive study, the performance parameters are considered such as PV array topology, the occurrence of partial shading, irradiation levels for investigation, capacity, and grid integration or standalone mode of power generation. The taxonomy of the PV systems under PSCs is depicted in Table II as, This work is licensed under a Creative Commons Attribution 4.0 License.…”

Multi-Objective Grey Wolf Optimizer for Optimal Design of Switching Matrix for Shaded PV array Dynamic Reconfiguration

“…The genetic algorithm, GA, has been used for enhancing the output power of the shaded PV array connected in TCT via the reconfiguration process [18]. three different optimizers including the flow regime algorithm (FRA), the social mimic optimization algorithm (SMO), and the Rao optimization algorithm have been proposed in [19] to provide the optimal design for the switching matrix.…”

Performance of Hybrid Filter in a Microgrid Integrated Power System Network Using Wavelet Techniques

“…Moreover, for tracking the maximum power in PV, MPPT techniques, such as perturb and observe (P&O) technique [23], adaptive variable step-size P&O [24], Fuzzy logic control (FLC), and Adaptive P&O-FLC technique [25], incremental conductance (INC) techniques [26], modified INC [27], adaptive step size INC [28], variable step size INC [29] are commonly used.…”