Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation

Ali, Fathelalem F.; Sarwar, Adil; Bakhsh, Farhad Ilahi; Ahmad, Shafiq; Shah, Adam Ali; Ahmed, Hafiz

doi:10.1016/j.enconman.2022.116613

Cited by 40 publications

(21 citation statements)

References 51 publications

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“…The DDM's best result is 6.835 × 10 −4 , provided by the Artificial Hummingbird Algorithm (AHA) [31]. The TDM best result is 7.950 × 10 −4 provided by the Atomic Orbital Search (AOS) [32]. This may confirm that no algorithm can give the best results for all types of problems.…”

Section: Photovoltaic Parameters Extractionmentioning

confidence: 90%

“…From this table, the parameter extraction of PV panels has been carried out for various commercialized types. Concerning the RTC France solar cell, the best result for the SDM has been provided by the Atomic Orbital Search (AOS) [32] with a fitness value of 7.752 × 10 −4 . The DDM's best result is 6.835 × 10 −4 , provided by the Artificial Hummingbird Algorithm (AHA) [31].…”

Section: Photovoltaic Parameters Extractionmentioning

confidence: 99%

“…The parameters of three models (SDM, DDM, and TDM) have been identified for this type using the Artificial Hummingbird Algorithm (AHA) [31]. A similar study has been proposed in [32] using Atomic Orbital Search (AOS), and another one in [33] by using the Marine Predators Optimizer (MPA). Kyocera KC200GT PV also recieved increased interest in the parameters identification context, such as Northern Goshawk Optimization (NGO) [34], Gorilla Troops Optimizer (GTO) [35], Transient Search Optimization (TSO) [36], and Coyote Optimization Algorithm (COA) [37].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

et al. 2023

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For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed.

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Section: Photovoltaic Parameters Extractionmentioning

confidence: 90%

Section: Photovoltaic Parameters Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

et al. 2023

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“…Although the above solution techniques have achieved varying degrees of success, the difficulty in parameter setting of these methods and the lack of design considerations for some optimization problems make them show different degrees of optimization deficiency in finding the optimal solution. [19][20][21][22] Thankfully, a series of heuristic algorithms with advanced theories have been successively proposed, including monarch butterfly optimization, 23 moth search algorithm, 24 hunger games search (HGS), 25 Runge-Kutta method, 26 colony predation algorithm, 27 weighted mean of vectors, 28 Harris Hawks Optimization (HHO), 29 rime optimization algorithm (RIME), 30 and the sine-cosine algorithm (SCA). 31 In addition, a number of combinatorial algorithms have been recognized, such as BOAALO 32 based on butterfly optimization algorithm, 33 and ant lion optimizer 34 ; CNNA-BES 35 based on convolutional neural network architecture and bald eagle search 36 optimization algorithm; QGBWOA 37 based on quasiopposition-based learning and Gaussian barebone mechanism; MOQBHHO 38 based on K-Nearest Neighbor method and multiobjective HHO.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

Zhou,

Shang

2024

Energy Science & Engineering

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Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

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Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Çetinbaş

2024

Global Challenges

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This study presents the parameter extraction of single, double, and triple‐diode photovoltaic (PV) models using the weighted leader search algorithm (WLS). The primary objective is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different PV cells, and 18 for six PV modules, whose experimental data are publicly available, are developed successfully. The second objective of this research is the selection of the most suitable algorithm for this problem. It is a significant challenge since the evaluation process requires using advanced statistical tools and techniques to determine the reliable selection. Therefore, seven brand‐new algorithms, including WLS, the spider wasp optimizer, the shrimp and goby association search, the reversible elementary cellular automata, the fennec fox optimization, the Kepler optimization, and the rime optimization algorithms, are tested. The WLS has yielded the smallest minimum, average, RMSE, and standard deviation among those. Its superiority is also verified by Friedman and Wilcoxon signed‐rank test based on 144 pairwise comparisons. In conclusion, it is demonstrated that the WLS is a superior algorithm in PV parameter extraction for developing accurate models.

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Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation

Cited by 40 publications

References 51 publications

A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

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