A new grey wolf optimization-based parameter estimation technique of solar photovoltaic

Rawat, Nikita; Thakur, Padmanabh; Singh, Asheesh Kumar; Bhatt, Ankit; Sangwan, Venu; Manivannan, Arthi

doi:10.1016/j.seta.2023.103240

Cited by 6 publications

(3 citation statements)

References 42 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: 59%

“…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: 59%

Section: Introductionmentioning

confidence: 99%

Parameter estimation of photovoltaic module relied on golden jackal optimization

2024

Archives of Electrical Engineering

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“…However, photovoltaic power generation devices are usually exposed to harsh outdoor environments and are highly susceptible to weather changes [5]. In order to ensure the high efficiency of photovoltaic conversion into electricity, more and more researchers have conducted in-depth research on the optimal parameters of the photovoltaic (PV) system [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm

Liu,

Yang,

Qin

et al. 2023

Sensors

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An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

Sulaiman,

Mustaffa

2024

Energy and AI

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A new grey wolf optimization-based parameter estimation technique of solar photovoltaic

Cited by 6 publications

References 42 publications

Parameter estimation of photovoltaic module relied on golden jackal optimization

Parameter estimation of photovoltaic module relied on golden jackal optimization

A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

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