Comparative Review of MPPT Algorithms

Silva, Martiane de Oliveira; Calili, Rodrigo Flora; Louzada, Daniel Ramos

doi:10.1109/pvsc43889.2021.9518853

Cited by 3 publications

(1 citation statement)

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“…To solve the drawbacks of these standard MPPT algorithms, metaheuristic-based methods are introduced. Particle swarm optimization, differential evolution, and genetic algorithm are the three most often used metaheuristics algorithms [4], [11]- [13]. As these metaheuristics algorithms search for all peaks over the whole P-V curve, it is probable that the GMPP will be identified.…”

Section: Metaheursitics-based Maximum Power Point Tracking Algortihmsmentioning

confidence: 99%

Metaheuristics-based maximum power point tracking for PV systems: a review

Jamhari,

Hashim,

Othman

et al. 2023

IJPEDS

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<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

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Section: Metaheursitics-based Maximum Power Point Tracking Algortihmsmentioning

confidence: 99%