Meta-Heuristic Optimization Techniques Used for Maximum Power Point Tracking in Solar PV System

Verma, Preeti; Alam, Afroz; Sarwar, Adil; Tariq, Mohd; Vahedi, Hani; Gupta, Deeksha; Ahmad, Shafiq; Mohamed, Adamali Shah Noor

doi:10.3390/electronics10192419

Cited by 46 publications

(17 citation statements)

References 100 publications

(126 reference statements)

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“…Traditional optimization methods have several drawbacks when solving complex and complicated problems that require considerable time and cost optimization. Metaheuristic algorithms have been proven capable of handling a variety of continuous and discrete optimization problems [46] in a wide range of applications including engineering [47][48][49], industry [50,51], image processing and segmentation [52][53][54], scheduling [55,56], photovoltaic modeling [57,58], optimal power flow [59,60], power and energy management [61,62], planning and routing problems [63][64][65], intrusion detection [66,67], feature selection [68][69][70][71][72], spam detection [73,74], medical diagnosis [75][76][77], quality monitoring [78], community detection [79], and global optimization [80][81][82]. In the following, some representative metaheuristic algorithms from the swarm intelligence category used in our experiments are described.…”

Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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“…ABC is a swarm based stochastic algorithm developed to solve multimode and multidimensional problems based on honeybee characteristics of food searching and have advantage that convergence not depends on initial conditions Baba et al (2020). In this algorithm, three types of bees are used namely employed bees for searching food source, onlooker bees for decision making and scout bees for improving food sources by number of trails Verma et al (2021), Motahhir et al (2020), Gonzalez-Castano et al (2021. In this algorithm, communication between bees takes place through pheromone and joggle dance.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on MPPT Algorithms for Solar Pv Systems

Senthilkumar

Mohan

Deepa

et al. 2023

Int. J. Res. Granthaalayah

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In past few decades, solar energy plays a vital role in energy production among the different renewable energy resources. In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works available on different types of MPPT techniques to track maximum power from PV systems with own pros and cons. This article comprehensively reviews the different traditional methods like perturb and observation (P&O), open circuit voltage (OCV), short circuit current (SCC), hill climbing (HC), incremental conductance (IC). Also recall the advanced MPPT techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), cuckoo search (CS), artificial neural networks (ANN), fuzzy logic controller (FLC) available in literature. This review is conducted based on implementation, accuracy, tracking speed, cost, merits, and demerits of each technique. Traditional MPPT methods can’t able to track global maximum power point under partial shaded conditions and exhibits less efficiency when compared with advanced soft computing methods. Hybrid methods provide good efficiency and performance than traditional and advanced methods. Authors powerfully confirm that this article offers convenient information’s to enthusiastic engineers and new researchers those who are all working in solar PV systems.

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“…1. Classi cation depending on the tracking algorithm [16,23,[27][28][29][30] 2. Classi cation based on the tracking nature [20,21,25] 3.…”

Section: Selection Synthesismentioning

confidence: 99%

MPPT techniques for PV systems: A systematic review in current trends and recent advances in AI

Boubaker

2023

Preprint

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Over the past decades, solar photovoltaic (PV) energy has been the most valuable green energy. It is renowned for its sustainability, environmentally friendly nature, and minimal maintenance costs. Several methods aiming to extract the highest photovoltaic energy are found in the vast literature. The aim of this systematic review is to focus on current trends and the most recent advances in the field. A “Scopus” bibliographic survey is conducted around survey and research articles published over the past three years (2019–2022) according to the PRISMA selection methodology. Over the selected works, different taxonomies of maximum power point tracking (MPPT) approaches are found. The list of associated performance criteria is also established, current trends, future directions and challenges in the field are well identified. This survey paper could be a useful reference for researchers and companies concerned by the sustainable development goals (GSD) for clean energy production and climate change.

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Meta-Heuristic Optimization Techniques Used for Maximum Power Point Tracking in Solar PV System

Cited by 46 publications

References 100 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

A Review on MPPT Algorithms for Solar Pv Systems

MPPT techniques for PV systems: A systematic review in current trends and recent advances in AI

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