Parameter extraction of solar PV cell models using novel metaheuristic chaotic tunicate swarm algorithm

Author(s):  
Jyoti Gupta ◽  
Parag Nijhawan ◽  
Souvik Ganguli
Author(s):  
Abhishek Sharma ◽  
Abhinav Sharma ◽  
Averbukh Moshe ◽  
Nikhil Raj ◽  
Rupendra Kumar Pachauri

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.


Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2094
Author(s):  
Shoeb Ahmad Khan ◽  
Shafiq Ahmad ◽  
Adil Sarwar ◽  
Mohd Tariq ◽  
Javed Ahmad ◽  
...  

The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.


Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 878
Author(s):  
Abhishek Sharma ◽  
Ankit Dasgotra ◽  
Sunil Kumar Tiwari ◽  
Abhinav Sharma ◽  
Vibhu Jately ◽  
...  

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.


2021 ◽  
Vol 43 ◽  
pp. 2843-2849 ◽  
Author(s):  
Bhuwan Pratap Singh ◽  
Sunil Kumar Goyal ◽  
Prakash Kumar
Keyword(s):  
Solar Pv ◽  
Pv Cell ◽  

2021 ◽  
Author(s):  
Mustajab Ali ◽  
Hyungjun Kim

<p>Solar Photovoltaic (PV) has the potential to fulfill a considerable amount of growing electricity demands worldwide.  In addition, being neat and clean, it can help to keep the greenhouse gases emission within safe limits. This resource needs a substantial amount of area for its sitting to supply the required amount of electricity. Such an area mainly depends on the available solar resource which is mainly the function of the local environment where PV is installed. Although some previous studies exist at the global scale, however, they have not comprehensively considered environmental (e.g., temperature, dust deposition, and snow) limiting factors that affect the actual solar PV yield. This study addresses such shortcomings and deals with all limiting factors simultaneously to provide a reliable assessment of potential PV performance at a global scale. PV cell efficiency is reduced due to an increase in resistance between cells at a temperature above a certain limit. Meanwhile, the accumulation of soil (dust) and snow on PV modules are also proven to limit the solar PV resources as it tends to block the incoming solar radiation. Lastly, the geomorphological parameter, which is an arrangement of a PV module to face the sun, is also shown to change its power output.</p><p>PV cell efficiency corrections for temperature changes, soil, and snow covers are applied using the biased corrected data from Global Soil Wetness Project 3 (GWSP3), CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2 from National Snow and Ice Data Center (nsidc), and TERRA/MODIS Aerosol Optical Thickness data available from NASA Earth Observations (NEO). The daily mean solar climatological values near the Earth’s surface for the last 14 years (2001–2014) with global coverage of 0.5º x0.5º are used in the analysis. The results have demonstrated that PV performance is affected by temperature increase, soil, snow, and varying tilt-angles. An annual maximum reduction of 5.7% in the total solar PV resource is seen in the Middle East due to the temperature changes. Likewise, a maximum loss of 6.45% in the total solar PV resource is witnessed for soil deposition for Sub-Saharan Africa. A higher reduction (~20%) is shown by snow covers for Russia and Canada in the upper Northern Hemisphere. In addition, a decline of 5–7% is observed for variation in the solar PV tilt-angles in comparison to optimum ones. As a whole, a maximum reduction of 19.45% in the total solar PV resource is found, which leads to a higher coefficient of determination (R<sup>2</sup>= 0.78) than uncorrected estimation (R<sup>2</sup>=0.67). This study will be helpful for household as well as large scale solar schemes and may contribute particularly to achieving the UN SDG No. 07 — Affordable and Clean Energy — and No. 13 — Climate Action — quantitatively.</p>


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