Symmetric chaotic gradient-based optimizer algorithm for efficient estimation of PV parameters

Khelifa, Mohammed Amin; Lekouaghet, Badis; Boukabou, Abdelkrim

doi:10.1016/j.ijleo.2022.168873

Cited by 11 publications

(4 citation statements)

References 68 publications

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“…In comparison with some previous methods, the optimal results obtained by GJO and some other methods for the KC200GT module at 1 000 W•m 2 and 25 • are presented in Table 11. The table shows that GJO finds a better RMSE value compared to PSO, HGSO, and the previous methods such as GWO [13], SSA [16], and CWOA [16], while the RMSE gained by GJO is slightly higher than those of SC-GBO [17] and WHHO [8]. This result confirms that the application of GJO for the PV module parameter estimation is reliable.…”

Section: Resultssupporting

confidence: 60%

“…The main strategy for finding the unknown PV parameters is adjusting the curve to predict the I-V curve, wherein the data points on the predicted I-V curve match with the experiment values. There are several heuristic methods used for the PV parameter estimation problem such as particle swarm optimization (PSO) [9,10], a genetic algorithm [11], cuckoo search [12], whippy Harris hawks optimization (WHHO) [8], grey wolf optimization (WGO) [13], musical chairs algorithm [14], arithmetic optimization algorithm [15], social spider algorithm (SSA) [16], symmetric chaotic gradient-based optimizer [17], as well as hybrid PSO and WGO [18]. It can be seen that the number of heuristic-based methods is larger than that of deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation of photovoltaic module relied on golden jackal optimization

2024

Archives of Electrical Engineering

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Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

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Section: Resultssupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Parameter estimation of photovoltaic module relied on golden jackal optimization

2024

Archives of Electrical Engineering

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“…Premkumar et al [83] and Khelifa et al [84] proposed an improved GBO using chaotic drifts (CGBO) to locate the optimal parameters of solar photovoltaic model. Five case studies were used to validate the efficiency of CGBO.…”

Section: Energy and Power Flowmentioning

confidence: 99%

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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“…Chaos theory not only shortens the processing time of skipping training data but also increases the amount of single data processing. erefore, the model constructed in this paper can classify a large number of training data [13] and analyze them continuously to form a continuous data link. In formula (5), the value is y, the relation function is C, and the classification coefficient is δ.…”

Section: Logistic Modelmentioning

confidence: 99%

Optimization of Skipping Rope Training Method Based on Chaotic Logistics

Li¹,

Wang²

2022

Mathematical Problems in Engineering

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Skipping training belongs to a competitive event, which can not only lose weight but also improve the physical quality of trainers. At present, there are some problems in skipping training, such as unreasonable plan and unsatisfactory actual training effect. The original rough set method cannot solve the analysis of multivariate data in skipping training, and the ability to evaluate the training effect of skipping is poor. Aiming at the problems existing in the training of skipping rope, this paper puts forward a skipping rope training method based on a chaotic logistics algorithm, aiming at improving the training effect of skipping rope. First, the chaos theory is used to classify the training data. Different data classifications correspond to different training results, which eliminates irrelevant information in the training scheme and reduces the amount of single data analysis. Finally, the data after classification are optimized in a single stage, and the optimal results of different training items are obtained and summarized. After the MATLAB test, the chaotic logistics algorithm is superior to the rough set method in accuracy and convergence speed and meets the actual training needs. Therefore, the chaotic logic analysis method proposed in this paper is suitable for the evaluation of skipping training.

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Symmetric chaotic gradient-based optimizer algorithm for efficient estimation of PV parameters

Cited by 11 publications

References 68 publications

Parameter estimation of photovoltaic module relied on golden jackal optimization

Parameter estimation of photovoltaic module relied on golden jackal optimization

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Optimization of Skipping Rope Training Method Based on Chaotic Logistics

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