Review on parameter estimation techniques of solar photovoltaic systems

Venkateswari, R.; Rajasekar, N.

doi:10.1002/2050-7038.13113

Cited by 48 publications

(18 citation statements)

References 196 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many optimisation algorithms have been applied with the purpose of drawing out the parameters of the PEMFCs [6,8,9,11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Moreover, the application of recent optimisa-tion algorithms is very interesting by many researchers for modelling the components of energy systems, such as PV cells [30,31]. Other researchers applied the optimisation algorithms for optimising other complex industrial systems [32].…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

show abstract

Section: Literature Review and Research Gapmentioning

confidence: 99%

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…The rapid development of population-based optimization algorithms has enhanced this research area, due to their being meta-heuristic and derivative-free algorithms, unlike greedy algorithms-which solve problems locally in each step, thus depending on finding the best solution in each step based on the previous step-and convex algorithms (e.g., gradient descent algorithms) that depend on the convex function. Therefore, population-based algorithms are suitable for complex and nonlinear functions such as the objective functions of PV models [20,21]. The main challenge in the estimation of PV models' parameters is the nonlinear characteristics of the PV systems, requiring a robust optimization algorithm for this optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…The main challenge in the estimation of PV models' parameters is the nonlinear characteristics of the PV systems, requiring a robust optimization algorithm for this optimization problem. A review of the recent work in this area was presented in [21]. In this study, a collection of recent work on the estimation of PV parameters is discussed.…”

Section: Introductionmentioning

confidence: 99%

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

View full text Add to dashboard Cite

The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.

show abstract

“…However, accurate modeling of PV cells is complicated by the non-linearity of PV, the presence of large unknown model parameters and the lack of proprietary methods. Determining their values has high priority since the number of related model parameters are directly related to the accuracy and efficiency of the model [4]. Solar energy is abundant and at the forefront, but its growth is hampered by factors such as partial shading, intermittent properties, high initial costs, and expensive storage requirements [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Ensemble of constraint handling techniques for PV parameter extraction using differential evolutionary algorithms

Kumari

Geethanjali

2022

IJPEDS

View full text Add to dashboard Cite

The depletion of fossil fuels and rising environmental concerns have paved the way for the development of clean renewable energy sources. Photovoltaic (PV) cells are represented by electrical equivalent circuits. Finding the right circuit model parameters for PV cells is critical task. Estimating accurate parameters helps in better performance assessment, control, efficiency calculation and maximum power point tracking. This manuscript describes a new approach for obtaining PV system parameters using ensemble of constraint handling techniques (ECHT) with evolutionary algorithms (EA). Four distinguished technologies of solar PV cells are considered to estimate the parameters with best accuracy. Experiments reveal that ECHT outperforms each individual constraint handling approach by competing with state-of-the-art algorithms. The experimental data for these Kyocera cells is compared with estimated values obtained from the proposed algorithm using MATLAB 2021B for different irradiation. The performance plots show excellent match between the real and simulated values. The root mean square error (RMSE) values for research tax credit RTC France were found to be 7.325513*10-4 and Kyocera processing the normalize RMSE of 0.414%. On comparison with recent algorithms the proposed method achieves the lowest root mean square error (RMSE) meeting the main objective of proposed work.

show abstract

Review on parameter estimation techniques of solar photovoltaic systems

Cited by 48 publications

References 196 publications

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

Ensemble of constraint handling techniques for PV parameter extraction using differential evolutionary algorithms

Contact Info

Product

Resources

About