A novel metaheuristic MPPT technique based on enhanced autonomous group Particle Swarm Optimization Algorithm to track the GMPP under partial shading conditions - Experimental validation

• Published in: Energy conversion and management Vol. 287; p. 117124
• Main Authors: Refaat, Ahmed, Khalifa, Abd-Elwahab, Elsakka, Mohamed Mohamed, Elhenawy, Yasser, Kalas, Ahmed, Elfar, Medhat Hegazy
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: administrative management; algorithms; Autonomous Group PSO (AGPSO); comparative study; energy efficiency; Enhanced AGPSO (EAGPSO); Fast change in solar radiation; Partial shading conditions; Particle Swarm Optimization (PSO); PV systems; solar collectors; solar radiation; Autonomous Group PSO (AGPSO); Enhanced AGPSO (EAGPSO); PV systems; Partial shading conditions; Fast change in solar radiation; Particle Swarm Optimization (PSO)
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2023.117124

•A novel metaheuristic MPPT technique based on an EAGPSO algorithm is proposed.•Performance of the EAGPSO is compared with other versions of PSO algorithms.•Simulation results illustrate the efficacy and fast-tracking time of the EAGPSO.•Experimental results validate the feasibility of the proposed EAGPSO.•The MPPT efficiency obtained is 98.6% with an experimental tracking time of 2.6 s. In order to maximize the benefit of solar panels, it is crucial to enhance the operating efficiency of PV systems. Usually, the PV arrays are exposed to harsh outdoor operating conditions that affect their operating efficiency. Extraction of global maximum power point (GMPP) among the other local MPPs (LMPPs) from the distorted characteristics of the PV array is considered the main challenge of PV system controllers under partial shading conditions (PSCs). Classical MPP tracking (MPPT) techniques have incompetent performance in tracing the GMPP under the PSCs of PV arrays. Therefore, many metaheuristics-based MPPT techniques are developed by researchers to defeat the problems related to the PSCs and cope with GMPP and other LMPPs. Among all metaheuristic algorithms, Particle Swarm Optimization (PSO) and its modified versions are the most common optimization methods owing to their easy implementation, simplicity, and low computational burden. On the other hand, the standard PSO (SPSO) algorithm suffers from low convergence speed, and difficulty in tuning its parameters, and it may be trapped in LMPP under fast changes in solar radiation and/or PSCs. In order to tackle these issues, a novel metaheuristic MPPT technique based on an Enhanced Autonomous Group PSO (EAGPSO) algorithm is proposed. A comparative study between EAGPSO and other versions of PSO techniques is performed to emphasize the efficacy of the EAGPSO technique. The simulation results demonstrate that the proposed EAGPSO algorithm is superior to the counterpart versions of the PSO algorithms in terms of fast-tracking time, high obtained power, and high energy efficiency. Experimental validation of the EAGPSO technique has been conducted using an array of 3*3 series–parallel PV panels under PSC. The EAGPSO algorithm results have been compared with SPSO and AGPSO techniques. The experimental results show that, among the investigated MPPT techniques, the proposed EAGPSO technique has the highest MPPT efficiency of 98.6% and the lowest power oscillations of about 2.03% of the tracked power with a tracking time of 2.6 s.