Global maximum power point tracking of solar PV strings using the teaching learning based optimisation technique

Nagadurga, T.; Narasimham, P. V. R. L.; Vakula, V. S.

doi:10.1080/01430750.2020.1721327

Cited by 16 publications

(5 citation statements)

References 18 publications

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“…In addition, the approach mimics instructors', and students' educational and cognitive capacities in the classroom as two vital components. It also describes the two fundamental methods of teaching, through the teacher phase and through interaction with other students (the student phase) [50].…”

Section: Teaching-learning-based Optimization (Tlbo)mentioning

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: Teaching-learning-based Optimization (Tlbo)mentioning

confidence: 99%

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

Jamhari,

Hashim,

Othman

et al. 2023

IJPEDS

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“…In addition, they are easy to be implement due to the reduced number of required parameters. Among these methods, we can mention: particle swarm optimisation (PSO) [12], ant colony optimisation (ACO) [13], [14], firefly algorithm (FA) [15], gray wolf optimisation (GWO) [16], Cuckoo search (CS) [17], whale optimisation algorithm (WOA) [18], salp swarm optimisation (SSO) [19], artificial bee colony (ABC) [20], Bat optimisation (BA) [21], [22], teaching learning based optimisation technique (TLBO) [23].…”

Section: Introductionmentioning

confidence: 99%

A bio inspired maximum power point tracking controller for PV systems under partial shading conditions

Sabrina¹,

Hadjer²,

Nouma

et al. 2022

IJEECS

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Maximum power point tracking (MPPT) is a technique used in extracting the maximum power from a photovoltaic panel (PV) under different weather conditions. The last decade has witnessed a wide variety of algorithm based on MPPT controllers, ranging from simple to more complexe ones. Each of them has its own advantage and disadvantage. Hence, it is crutial to propose methods that are both simple and effective to track and maintain the MPPT of a PV system, even under partial shading conditions. In this study, we propose a new bio inspired method namely seagull optimization algorithm (SOA) for solving the MPPT problem in a PV system. To evaluate the proposed SOA _MPPT performance in terms of accuracy, convergence and stability, a simulation methodology is used. First, by tunning the appropriate parameters, then, we consider the following scenarios: rapid change of solar irradiation, temperature, and three patterns to test partial shading effect. The results are compared with latest bio-inspired methods, namely, particle swarm optimization (PSO), Bat optimisation algorithm (BAT) and fire fly algorithm (FA). The obtained results confirm the effectiveness and robustness of the proposed controller compared to existing conventional and bio inspired controllers.

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“…Sundareswaran et al [24] proposed a novel artificial bee colony for extracting GMPP under shaded climate situations. Nagadurga et al [25] compared the teaching-learning-based optimization (TLBO) algorithm with the PSO technique, and the authors showed that implementation of TLBO is easy due to its simple tuning parameters. Helwa et al [26] investigated different ways to track the sun and extract maximum energy for improving the input given to the power converter.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the standalone PSO and GWO techniques are quite successful in achieving efficient MPP tracking performance. It has been noted from various literature studies that PSO, GWO and some of its variants facing the problem of stuck in local peak remains to persist under shading conditions by the works of [22][23][24][25]. The satisfactory presentation of optimization techniques mainly depends on tuning their algorithm parameters and exploration capabilities.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using Chimp Optimization Algorithm

Nagadurga

Narasimham²,

Vakula

et al. 2021

Energies

Self Cite

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This paper proposes the application of a metaheuristic algorithm inspired by the social behavior of chimps in nature, called Chimp Optimization Algorithm (ChOA), for the maximum power point tracking of solar photovoltaic (PV) strings. In this algorithm, the chimps hunting process is mathematically articulated, and new mechanisms are designed to perform the exploration and exploitation. To evaluate the ChOA, it is applied to some fixed dimension benchmark functions and engineering problem application of tracking maximum power from solar PV systems under partial shading conditions. Partial shading condition is a common problem that appears in the solar PV modules installed in domestic areas. This shading alters the power developed by the solar PV panel, and exhibits multiple peaks on the power variation with voltage (P-V) characteristic curve. The dynamics of the solar PV system have been considered, and the mathematical model of a single objective function has been framed for tuning the optimal control parameter with the suggested algorithm. Implementing various practical shading patterns of solar PV systems with the ChOA algorithm has shown improved solar power point tracking performance compared to other algorithms in the literature.

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Global maximum power point tracking of solar PV strings using the teaching learning based optimisation technique

Cited by 16 publications

References 18 publications

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

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

A bio inspired maximum power point tracking controller for PV systems under partial shading conditions

Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using Chimp Optimization Algorithm

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