A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models

Zhao, Juan; Zhang, Yu-Jun; Li, Shuijia; Wang, Yufei; Yan, Yu-Xin; Gao, Zheng-Ming

doi:10.3934/mbe.2022264

Cited by 14 publications

(4 citation statements)

References 68 publications

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“…Traditional evolutionary techniques, especially the JAYA algorithm, have been widely adapted for PV model parameter identification. Modified versions of JAYA [9][10][11][12] have been proposed, incorporating adaptive weights to balance local and global searches [13][14][15]. Additionally, strategies like chaotic elite learning [13], linear population reduction [14], and DE with chaotic perturbation [15] have been introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Modified versions of JAYA [9][10][11][12] have been proposed, incorporating adaptive weights to balance local and global searches [13][14][15]. Additionally, strategies like chaotic elite learning [13], linear population reduction [14], and DE with chaotic perturbation [15] have been introduced. To enhance population diversity, a novel elite opposition-based mechanism [16] has been integrated.…”

Section: Introductionmentioning

confidence: 99%

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A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Zhang,

Yang,

et al. 2024

IEEE Access

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Converting solar energy to electricity efficiently requires an accurate model with well-estimated parameters in a photovoltaic (PV) system. However, identifying parameters for the PV model remains challenging attributed to the nonlinearity and multimodality inherent in diode circuit models. In this paper, we propose a dual-feedback adaptive clone selection algorithm with a golden sine search. The clonal selection algorithm (CSA), known for its inherent diversity maintenance ability, offers an advantage in handling multimodal optimization problems. The critical factor is the cloning operator, particularly the determination of clone size, crucial for balancing exploration and exploitation. To address the conflict between clone size and convergence speed, we introduce a dual feedback adaptive cloning strategy. This strategy considers both the fitness difference between the hypermutated population and the original population and the harmonic mean distance of the population. Additionally, to improve accuracy, we introduce an elite learning strategy based on the golden sine mechanism. The proposed algorithm's effectiveness is assessed through comprehensive experiments on PV cells and three PV modules, considering both single diode and double diode models, respectively. The experimental results demonstrate that the accuracy of parameter identification achieved by the proposed algorithm exceeds 99.9% for all three types of PV modules. Moreover, we validate the algorithm's performance using manufacturer's datasheets with varying irradiations and temperatures. All experiments consistently indicate the stability and effectiveness of the proposed method in accurately identifying the parameters of PV models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Zhang,

Yang,

et al. 2024

IEEE Access

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“…For parameter extraction, the chaotic self-adaptive JAYA algorithm (AHJAYA) was used in photovoltaic models (R.T.C. France solar cell, STM6-40/ 36, and STP6-120/36) [49]. Te modifed fower algorithm (MFA) was validated on both models: SDM and DDM [50].…”

Section: Introductionmentioning

confidence: 99%

A Hunter-Prey Algorithm Coordinating Mutual Benefit and Sharing and Interactive Learning for High-Efficiency Design of Photovoltaic Models

Peng

et al. 2023

International Journal of Intelligent Systems

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It is crucial for the photovoltaic system to have an accurate model and well-estimated parameters to further increase conversion efficiency. Most existing methods for identifying photovoltaic model parameters have problems such as high computational cost, local optimum trouble, or difficulty in providing the best performance due to complex adjustments of algorithm parameters. To improve these defects, a hunter-prey optimization algorithm coordinating mutual benefit and sharing and interactive learning activities (EHPO) is proposed. First, based on hunter-prey optimization, timely information sharing with the mutual benefit and sharing mechanism was achieved when the algorithm was applied to search for prey, thus improving the searching precision and convergence rate of the algorithm. Second, hunters used the history optimization information and useful information from peers to guide the search direction, thus balancing the global and local development abilities of the algorithm. Finally, a lens imaging backward learning strategy was adopted to prevent the algorithm from falling into the local optimum, thus increasing the diversity of varieties and the probability of finding the global optimal solution. The simulation results of the single-diode model (SDM), double-diode model (DDM), triple-diode model (TDM), and other PV models in different environmental conditions show that the improved EHPO algorithm is more advantageous for parameter extraction than other advanced metaheuristic algorithms. This study demonstrates that EHPO is an accurate and reliable tool for predicting the parameters of PV models.

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“…Yu et al 70 combined the mutation and crossover operations of the sine–cosine method and a differential evolution algorithm based on the original gradient optimization method to design a method that has more potential in solving the problem of PV model parameter identification. Zhao et al 71 combined a linear population reduction strategy with an opposition‐based learning strategy to improve the JAYA algorithm, and designed adaptive coefficients to propose a highly competitive method for solving the extraction of unknown parameters from a PV system. Although there has been much representative work in this area, the optimization accuracy achieved in solving the problem cannot be overstated, as building an accurate model is crucial for optimizing and preventing PV systems.…”

Section: Introductionmentioning

confidence: 99%

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

Weng

Liu

Heidari

et al. 2022

Energy Science & Engineering

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The determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning-based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation

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A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models

Cited by 14 publications

References 68 publications

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A Hunter-Prey Algorithm Coordinating Mutual Benefit and Sharing and Interactive Learning for High-Efficiency Design of Photovoltaic Models

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

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