Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

Al-Shamma’a, Abdullrahman A.; Omotoso, Hammed Olabisi; Alturki, Fahd A.; Farh, Hassan M. H.; Alkuhayli, Abdulaziz; AlSharabi, Khalil; Noman, Abdullah M.

doi:10.3390/en15010140

Cited by 21 publications

(14 citation statements)

References 45 publications

(71 reference statements)

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“…The BO is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos [250]. The algorithm has a simple implementation process and a fast computational time [251]. In order to further enhance its performance, an Improved Bonobo algorithm (IBO) was developed in [117] by incorporating the Levy flight distribution and three leaders selection.…”

Section: ) Bonobo Algorithm (Bo)mentioning

confidence: 99%

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

Foqha,

Alsadi,

Omari

et al. 2024

IEEE Access

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This paper provides a comprehensive review of optimization techniques for coordinating directional overcurrent relays in power systems. It covers a wide range of techniques, including conventional and deterministic methods, metaheuristic algorithms, and hybrid approaches. The paper discusses the objective functions utilized in formulating relay coordination problem and presents the development of optimization methods for solving this problem. Furthermore, it examines the criteria for comparing different algorithms for coordination problem and includes a case study to demonstrate the practical application of these criteria. It also presents simulation software employed for examining and validating results obtained from optimization algorithms. Future trends and challenges regarding optimal coordination of directional overcurrent relays are also discussed. The paper concludes that there is no single "best" optimization technique for the coordination problem. The best technique for a particular application will depend on the specific characteristics of the power system and the constraints of the coordination problem.

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Section: ) Bonobo Algorithm (Bo)mentioning

confidence: 99%

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

Foqha,

Alsadi,

Omari

et al. 2024

IEEE Access

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“…Hybrid Rao-1 (HROA) optimization [34], featuring a chaotic logistic map and an exploration operator, is proposed. The Bonobo metaheuristic optimizer (BO) [35], known for its short computation time and simple implementation, is introduced. A viscous mushroom algorithm (SMA) [36], integrating the Nelder-Mead simplex strategy and chaotic mapping, enhances intensification and exploration capabilities.…”

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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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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“…Because it is gradient-free, simple to create, and performs well, this approach is perfect for dealing with difficult optimization issues. These approaches of problem resolution include SDBT [17,18], PSO [19], sunflower optimization technique [20], MPA [21], social networking searching method [22], butterfly optimizing approach [23], bonobo optimizer [24], HBM [25], etc. In [26], an IGA with a non-uniform mutation was presented for retrieving features from PV models in a trustworthy, exact, and time-efficient way.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

Moustafa

2023

Eng

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Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms.

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Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

Cited by 21 publications

References 45 publications

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

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

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

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