Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm

El‐Dabah, Mahmoud A.; El‐Sehiemy, Ragab A.; Hasanien, Hany M.; Saad, Bahaa

doi:10.1016/j.energy.2022.125522

Cited by 80 publications

(35 citation statements)

References 54 publications

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“…The irradiance was measured using an SP‐110‐SS silicon‐cell pyranometer. A high‐probe infrared electronic thermometer temperature gauge with a 1‐C precision is used to measure the temperature [65].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The irradiance was measured using an SP-110-SS silicon-cell pyranometer. A high-probe infrared electronic thermometer temperature gauge with a 1-C precision is used to measure the temperature [65]. The KC200GT PV module's nine parameters are extracted using the INFO optimizer, and it exhibits superior convergence properties compared with the algorithm listed in Table 3 as its fitness function reaches 9.0738 × 10 −06 .…”

Section: Kyocera-kc200gtmentioning

confidence: 99%

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Photovoltaic model parameters identification using an innovative optimization algorithm

El‐Dabah

El‐Sehiemy

Hasanien

et al. 2023

IET Renewable Power Gen

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As it tackles electrical and non‐electrical losses, the triple‐diode model (TDM) of photovoltaic (PV) cells is highly exact. This paper employs a novel optimization method known as the innovative optimization algorithm (INFO) technique to correctly estimate the electrical characteristics of such TDM. To shift agents towards a better position, the INFO algorithm exploits the concept of weighted mean. The primary goal of INFO is to stress its performance features to solve some optimization difficulties that other approaches cannot effectively solve. In this paper, the objective function based on a combination of the absolute value of the current error, its squared value, and its quadrable value is employed, which the INFO optimizer minimizes to predict the optimum parameters of such TDM precisely. The proposed INFO algorithm is carried out on multi‐ and mono‐crystalline varieties, such as the Kyocera KC200GT and the Canadian Solar CS6K‐280 M. The simulation outcomes demonstrate the INFO's ability to extract the model parameters precisely. The INFO achieved the lowest ideal fitness values of 9.0738 × 10−06 and 5.7356 × 10−05 for the KC200GT and Canadian Solar CS6K‐280 M, respectively, throughout the optimization procedure. Under various environmental circumstances, experimental validation of the calculated parameters using the (INFO) optimizer is carried out, and the results are compared to the observed values from the laboratory experiments. The simulation results demonstrate the INFO's convergence time and accuracy advantage over competing optimization techniques. Additionally, statistical analysis shows that the INFO optimizer is resilient.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Kyocera-kc200gtmentioning

confidence: 99%

Photovoltaic model parameters identification using an innovative optimization algorithm

El‐Dabah

El‐Sehiemy

Hasanien

et al. 2023

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

show abstract

“…The DDM best result is 2.404 × 10 −3 provided by Coyote Optimization Algorithm (COA) [37]. The best result of the TDM is provided by the Northern Goshawk Optimization (NGO) [34] with a final fitness of 1.346 × 10 −5 .…”

Section: Photovoltaic Parameters Extractionmentioning

confidence: 99%

“…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]. On the other hand, papers that present battery parameter extraction strategies are more frequently published.…”

Section: Introductionmentioning

confidence: 99%

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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“…In this subsection, the supremacy of the hybrid GTO-GBO technique is demonstrated using 23 benchmark iterations considered for all applied techniques is 200 and the number of populations is 50. In this subsection, the hybrid GTO-GBO algorithm is compared with several recent algo-rithms such as conventional GTO, GBO, artificial rabbits optimization (ARO) [54], and northern goshawk optimization (NGO) [55], [56] and the superiority of the achieved solution is tested using mean value and standard deviation (std functions in terms of the average value (F1, F3, F5, F7, F8, F9, F10, F11, F13, F14, F16, F17, F19, F21, F22, F23). It is clear from these results that the GTO-GBO algorithm can attain better solutions compared to several newly proposed algorithms in solving numerous of the benchmark functions.…”

Section: A Benchmark Functions Validationmentioning

confidence: 99%

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

et al. 2023

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This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm's considerable promise. This demonstrates the GTO-GBO algorithm's improved balance of global and local search stages. The proposed GTO-GBO algorithm's performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case's findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

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Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm

Cited by 80 publications

References 54 publications

Photovoltaic model parameters identification using an innovative optimization algorithm

Photovoltaic model parameters identification using an innovative optimization algorithm

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

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

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