Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators – success history based adaptive differential evolution algorithm

Naraharisetti, Jaya Naga Lakshmi; Devarapalli, Ramesh; Bathina, Venkateswararao

doi:10.1080/15567036.2020.1806956

Cited by 26 publications

(13 citation statements)

References 40 publications

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“…An adaptive EA was hybridized with the MPA to modify and enhance the MPA searching behaviour, skipping local optima and balancing local and global search was proposed by [ 122 ]. An adaptive version of EA was utilized to find the best parameter values that could achieve the optimal performance of the MPA.…”

Section: Variants Of Marine Predators Algorithmmentioning

confidence: 99%

Marine Predators Algorithm: A Review

Al-Betar

Awadallah

Makhadmeh

et al. 2023

Arch Computat Methods Eng

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Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.

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Section: Variants Of Marine Predators Algorithmmentioning

confidence: 99%

Marine Predators Algorithm: A Review

Al-Betar

Awadallah

Makhadmeh

et al. 2023

Arch Computat Methods Eng

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“…Recently, MPA and SHADE algorithm were effectively combined for the optimal parameter values selection of SDM and DDM of monocrystalline, thin-film, and polycrystalline [16]. This algorithm was experimentally compared with some of the optimization techniques to check its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

et al. 2021

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The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.

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“…MPA has the advantage of fewer control parameters, more simple calculation and easier implementation. At present, it is used to solve engineering optimization problems including the multilevel threshold segmentation (MLT) of images, 24 the optimal parameter combination in the single diode model, 25 the optimal parameter distribution of photovoltaic losses in photovoltaic power plants, 26 0–1 knapsack problem, 27 and so on. These applications show that MPA is a very competitive optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

A multigroup marine predator algorithm and its application for the power system economic load dispatch

Pan

Shan

Chu

et al. 2021

Energy Science & Engineering

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Marine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.

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Parameter extraction of solar photovoltaic module by using a novel hybrid marine predators – success history based adaptive differential evolution algorithm

Cited by 26 publications

References 40 publications

Marine Predators Algorithm: A Review

Marine Predators Algorithm: A Review

An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

A multigroup marine predator algorithm and its application for the power system economic load dispatch

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