A new maximum power point tracking technique based on whale optimisation algorithm for solar photovoltaic systems

Premkumar, M.; Kumar, C.; Anbarasan, A.; Sowmya, R.

doi:10.1080/01430750.2021.1969270

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…where D denotes the mean coil dia, d denotes the wire dia, and N denotes active coils. This problem also has four equality constraints, as listed in Equation (33). The bounds of variables are 0.05 ≤ d ≤ 2, 0.25 ≤ D ≤ 1.3, and 2 ≤ N ≤ 15.…”

Section: Tension/compression Spring Design Problemmentioning

confidence: 99%

“…One illustrative instance is harmony search [24], which emulates the improvisational tactics employed by musicians. Several other widely used swarm intelligence algorithms include the krill-herd [25], artificial bee colony [26], cuckoo search algorithm [27], biogeography-based optimization [28], grey wolf optimizer (GWO) [29][30][31], whale optimization algorithm [32][33][34], dragon-fly algorithm [35], ant colony optimization [36], dolphin echolocation algorithm [37], firefly algorithm [38], slime mould algorithm [39][40][41], marine predator algorithm [42][43][44], mountain gazelle optimizer [45,46], African vulture algorithm [47], artificial rabbits optimizer [48], etc. The authors of [49] have used an improved sparrow search algorithm to estimate the parameters of the carbon fiber drawing process.…”

Section: Introductionmentioning

confidence: 99%

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MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Kailasam,

Nalliah,

Nallagoundanpalayam Muthusamy

et al. 2023

Biomimetics

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In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

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Section: Tension/compression Spring Design Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Kailasam,

Nalliah,

Nallagoundanpalayam Muthusamy

et al. 2023

Biomimetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the accuracy of SDM and DDM are less than the TDM circuit model, and therefore, the modeling of TDM is discussed in this section. Further, the fitness function to estimate the parameters of the TDM is formulated along with the concept of the Newton-Raphson method [33,94,95].…”

Section: Mathematical Modeling and Problem Formulationmentioning

confidence: 99%

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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Parameter identification and accurate photovoltaic (PV) modeling from basic I-V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multidimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton-Raphson (NR) technique is discussed to solve the chaotic behavior of the I-V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman's test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

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“…The literary works processes and the new VVS-based GMPP strategy would then allow the researchers to pick the best strategy for their specific needs [30]- [33]. The researchers in the works [34], [35] allow us to understand the new algorithm used for partially shaded conditions based on humpback whale assisted and whale optimization for different partially shaded conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

Ravi

Premkumar

Abualigah

2023

IJAPE

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<span lang="EN-US">The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.</span>

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A new maximum power point tracking technique based on whale optimisation algorithm for solar photovoltaic systems

Cited by 10 publications

References 23 publications

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

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