# A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

- The combination of both analytical and numerical techniques (combined technique) are observed to be less time-consuming compared to other techniques that are used for parameter estimation of solar PV systems;
- The combined technique has the highest accuracy compared to others in the existing literature;
- In analytical algorithms, the number of non-linear exponential terms can be reduced by four times compared to the existing literature. Hence, the combined technique is computationally efficient;
- The shunt resistance (${R}_{sh}$) should be considered as one of the iteration parameters, which makes the approach more realistic;
- The application of the nominal operating cell temperature (NOCT) value in the PV parameter estimation strengthens the accuracy in varying temperatures and irradiance conditions. It is noticed that, on average, a 10% performance degradation (PD) is present in the MPP obtained at ${T}_{cell}$ compared to ${T}_{amb}$.

## 2. Solar PV Modelling

#### 2.1. Single-Diode Model

#### 2.2. Double-Diode Model

#### 2.3. Three-Diode Model

## 3. Algorithms Reported So Far

#### 3.1. Analytical Algorithms

#### 3.2. Non-Analytical Algorithms

#### 3.3. Meta-Heuristic Approach

- ${x}_{i}$ is the position of the $ith$ foodTable;
- l is the lower boundary;
- u is the upper boundary;
- r is the random vector in (0, 1).

## 4. Performance Analysis of the Reported Algorithms

#### Effect of Temperature and Irradiance

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- International, E.A. Photovoltaic Power Systems Programme; Snapshot: Paris, France, 2020; Available online: https://iea-pvps.org/snapshot-reports/snapshot-2020/ (accessed on 16 April 2022).
- Herrmann, W.; Wiesner, W. Modelling of PV modules—The effects of non-uniform irradiance on performance measurements with solar simulators. In Proceedings of the 16th European Photovoltaic Solar Energy Conference, Glasgow, UK, 1–5 May 2000; pp. 2338–2341. [Google Scholar]
- Goswami, A.; Sadhu, P.K. Nature inspired evolutionary algorithm integrated performance assessment of floating solar photovoltaic module for low-carbon clean energy generation. Sustain. Oper. Comput.
**2022**, 3, 67–82. [Google Scholar] [CrossRef] - Yu, Y.; Wang, K.; Zhang, T.; Wang, Y.; Peng, C.; Gao, S. A population diversity-controlled differential evolution for parameter estimation of solar photovoltaic models. Sustain. Energy Technol. Assess.
**2022**, 51, 101938. [Google Scholar] [CrossRef] - Venkateswari, R.; Rajasekar, N. Review on parameter estimation techniques of solar photovoltaic systems. Int. Trans. Electr. Energy Syst.
**2021**, 31, e13113. [Google Scholar] [CrossRef] - Mehta, H.K.; Warke, H.; Kukadiya, K.; Panchal, A.K. Accurate Expressions for Single-Diode-Model Solar Cell Parameterization. IEEE J. Photovol.
**2019**, 9, 803–810. [Google Scholar] [CrossRef] - Moshksar, E.; Ghanbari, T. Constrained optimisation approach for parameter estimation of PV modules with single-diode equivalent model. IET Renew. Power Gener.
**2018**, 12, 1398–1404. [Google Scholar] [CrossRef] - Arabshahi, M.; Torkaman, H.; Keyhani, A. A method for hybrid extraction of single-diode model parameters of photovoltaics. Renew. Energy
**2020**, 158, 236–252. [Google Scholar] [CrossRef] - Ridha, H.M.; Hizam, H.; Mirjalili, S.; Othman, M.L.; Ya’acob, M.E.; Abualigah, L. A novel theoretical and practical methodology for extracting the parameters of the single and double diode photovoltaic models. IEEE Access
**2022**, 10, 11110–11137. [Google Scholar] [CrossRef] - Yahya-Khotbehsara, A.; Shahhoseini, A. A fast modeling of the double-diode model for PV modules using combined analytical and numerical approach. Sol. Energy
**2018**, 162, 403–409. [Google Scholar] [CrossRef] - Gnetchejo, P.J.; Essiane, S.N.; Ele, P.; Wamkeue, R.; Wapet, D.M.; Ngoffe, S.P. Important notes on parameter estimation of solar photovoltaic cell. Energy Convers. Manag.
**2019**, 197, 111870. [Google Scholar] [CrossRef] - Sinha, A.; Gopalakrishna, H.; Subramaniyan, A.B.; Jain, D.; Oh, J.; Jordan, D.; TamizhMani, G. Prediction of Climate-Specific Degradation Rate for Photovoltaic Encapsulant Discoloration. IEEE J. Photovolt.
**2020**, 10, 1093–1101. [Google Scholar] [CrossRef] - Chan, D.S.; Phang, J.C. Analytical methods for the extraction of solar-cell single-and double-diode model parameters from IV characteristics. IEEE Trans. Electron Devices
**1987**, 34, 286–293. [Google Scholar] [CrossRef] - Phang, J.; Chan, D.; Phillips, J. Accurate analytical method for the extraction of solar cell model parameters. Electron. Lett. IET
**1984**, 20, 406–408. [Google Scholar] [CrossRef] - Changmai, P.; Nayak, S.K.; Metya, S.K. Estimation of PV module parameters from the manufacturer’s datasheet for MPP estimation. IET Renew. Power Gener.
**2020**, 14, 1988–1996. [Google Scholar] [CrossRef] - Hsieh, Y.C.; Yu, L.R.; Chang, T.C.; Liu, W.C.; Wu, T.H.; Moo, C.S. Parameter Identification of One-Diode Dynamic Equivalent Circuit Model for Photovoltaic Panel. IEEE J. Photovolt.
**2019**, 10, 219–225. [Google Scholar] [CrossRef] - Huang, Y.C.; Huang, C.M.; Chen, S.J.; Yang, S.P. Optimization of Module Parameters for PV Power Estimation Using a Hybrid Algorithm. IEEE Trans. Sustain. Energy
**2019**, 11, 2210–2219. [Google Scholar] [CrossRef] - Bradaschia, F.; Cavalcanti, M.C.; do Nascimento, A.J.; da Silva, E.A.; de Souza Azevedo, G.M. Parameter Identification for PV Modules Based on an Environment-Dependent Double-Diode Model. IEEE J. Photovolt.
**2019**, 9, 1388–1397. [Google Scholar] [CrossRef] - Zhang, Z.; Hu, G.; Chen, Q.; Yan, Z. Correntropy-based parameter estimation for photovoltaic array model considering partial shading condition. IET Renew. Power Gener.
**2019**, 13, 1309–1316. [Google Scholar] [CrossRef] - Guo, S.; Abbassi, R.; Jerbi, H.; Rezvani, A.; Suzuki, K. Efficient maximum power point tracking for a photovoltaic using hybrid shuffled frog-leaping and pattern search algorithm under changing environmental conditions. J. Clean. Prod.
**2021**, 297, 126573. [Google Scholar] [CrossRef] - Fathy, A.; Abd Elaziz, M.; Sayed, E.T.; Olabi, A.; Rezk, H. Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm. Energy
**2019**, 188, 116025. [Google Scholar] [CrossRef] - Tang, S.; Jiang, M.; Abbassi, R.; Jerbi, H.; Latifi, M. A cost-oriented resource scheduling of a solar-powered microgrid by using the hybrid crow and pattern search algorithm. J. Clean. Prod.
**2021**, 313, 127853. [Google Scholar] [CrossRef] - Ye, X.; Liu, W.; Li, H.; Wang, M.; Chi, C.; Liang, G.; Chen, H.; Huang, H. Modified whale optimization algorithm for solar cell and PV module parameter identification. Complexity
**2021**, 2021, 8878686. [Google Scholar] [CrossRef] - Eslami, M.; Akbari, E.; Seyed Sadr, S.T.; Ibrahim, B.F. A novel hybrid algorithm based on rat swarm optimization and pattern search for parameter extraction of solar photovoltaic models. Energy Sci. Eng.
**2022**. [Google Scholar] [CrossRef] - Xiong, G.; Zhang, J.; Yuan, X.; Shi, D.; He, Y. Application of symbiotic organisms search algorithm for parameter extraction of solar cell models. Appl. Sci.
**2018**, 8, 2155. [Google Scholar] [CrossRef] - Sharma, A.; Dasgotra, A.; Tiwari, S.K.; Sharma, A.; Jately, V.; Azzopardi, B. Parameter extraction of photovoltaic module using tunicate swarm algorithm. Electronics
**2021**, 10, 878. [Google Scholar] [CrossRef] - Sharma, A.; Sharma, A.; Moshe, A.; Raj, N.; Pachauri, R.K. An effective method for parameter estimation of solar PV cell using Grey-wolf optimization technique. Int. J. Math. Eng. Manag. Sci.
**2021**, 6, 911. [Google Scholar] [CrossRef] - Gude, S.; Jana, K.C. Parameter extraction of photovoltaic cell using an improved cuckoo search optimization. Sol. Energy
**2020**, 204, 280–293. [Google Scholar] [CrossRef] - Prasanth Ram, J.; Pillai, D.S.; Rajasekar, N.; Kumar Chinnaiyan, V. Flower pollination based solar PV parameter extraction for double diode model. In Intelligent Computing Techniques for Smart Energy Systems; Springer: Berlin, Germany, 2020; pp. 303–312. [Google Scholar]
- Zhen, Z.; Pang, S.; Wang, F.; Li, K.; Li, Z.; Ren, H.; Shafie-khah, M.; Catalão, J.P. Pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting. IEEE Trans. Ind. Appl.
**2019**, 55, 3331–3342. [Google Scholar] [CrossRef] - Changmai, P.; Kumar, S.; Nayak, S.K.; Metya, S.K. Maximum Power Estimation of Total Cross-Tied Connected PV Cells in different Shading Conditions for High Current Application. IEEE J. Emerg. Sel. Top. Power Electron.
**2021**, 10, 3883–3894. [Google Scholar] [CrossRef] - Easwarakhanthan, T.; Bottin, J.; Bouhouch, I.; Boutrit, C. Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Sol. Energy
**1986**, 4, 1–12. [Google Scholar] [CrossRef] - Gao, S.; Wang, K.; Tao, S.; Jin, T.; Dai, H.; Cheng, J. A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Convers. Manag.
**2021**, 230, 113784. [Google Scholar] [CrossRef] - Tran, T.-H.; Nguyen, H.; Nhat-Duc, H.; Nguyen, T.-D. A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete. Eng. Comput.
**2021**, 37, 1485–1498. [Google Scholar] - Yeh, J.F.; Chen, T.Y.; Chiang, T.C. Modified l-shade for single objective real-parameter optimization. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 10–13 June 2019; pp. 381–386. [Google Scholar]
- Biswas, P.P.; Suganthan, P.N. Large initial population and neighborhood search incorporated in lshade to solve cec2020 benchmark problems. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 19–24 July 2020; pp. 1–7. [Google Scholar]
- Refaat, M.M.; Aleem, S.H.A.; Atia, Y.; Ali, Z.M.; El-Shahat, A.; Sayed, M.M. A mathematical approach to simultaneously plan generation and transmission expansion based on fault current limiters and reliability constraints. Mathematics
**2021**, 9, 2771. [Google Scholar] [CrossRef] - Wang, X.; Zhao, H.; Han, T.; Wei, Z.; Liang, Y.; Li, Y. A Gaussian estimation of distribution algorithm with random walk strategies and its application in optimal missile guidance handover for multi-UCAV in over-the-horizon air combat. IEEE Access
**2019**, 7, 43298–43317. [Google Scholar] [CrossRef] - Mohamed, A.W.; Hadi, A.A.; Jambi, K.M. Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol. Comput.
**2019**, 50, 100455. [Google Scholar] [CrossRef] - Chen, H.; Cheng, R.; Wen, J.; Li, H.; Weng, J. Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations. Inf. Sci.
**2020**, 509, 457–469. [Google Scholar] [CrossRef] - Wei, Z.; Huang, C.; Wang, X.; Zhang, H. Parameters identification of photovoltaic models using a novel algorithm inspired from nuclear reaction. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 10–13 June 2019; pp. 210–218. [Google Scholar]
- Nunes, H.; Pombo, J.; Bento, P.; Mariano, S.; Calado, M. Collaborative swarm intelligence to estimate PV parameters. Energy Convers. Manag.
**2019**, 185, 866–890. [Google Scholar] [CrossRef] - Toledo, F.J.; Blanes, J.M.; Galiano, V. Two-step linear least-squares method for photovoltaic single-diode model parameters extraction. IEEE Trans. Ind. Electron.
**2018**, 65, 6301–6308. [Google Scholar] [CrossRef] - Diab, A.A.Z.; Sultan, H.M.; Aljendy, R.; Al-Sumaiti, A.S.; Shoyama, M.; Ali, Z.M. Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules. IEEE Access
**2020**, 8, 119668–119687. [Google Scholar] [CrossRef] - Yu, K.; Qu, B.; Yue, C.; Ge, S.; Chen, X.; Liang, J. A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Appl. Energy
**2019**, 237, 241–257. [Google Scholar] [CrossRef] - Yu, K.; Liang, J.; Qu, B.; Chen, X.; Wang, H. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers. Manag.
**2017**, 150, 742–753. [Google Scholar] [CrossRef] - Liang, J.; Ge, S.; Qu, B.; Yu, K.; Liu, F.; Yang, H.; Wei, P.; Li, Z. Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models. Energy Convers. Manag.
**2020**, 203, 112138. [Google Scholar] [CrossRef] - Kler, D.; Goswami, Y.; Rana, K.; Kumar, V. A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer. Energy Convers. Manag.
**2019**, 187, 486–511. [Google Scholar] [CrossRef] - Ebrahimi, S.M.; Salahshour, E.; Malekzadeh, M.; Gordillo, F. Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm. Energy
**2019**, 179, 358–372. [Google Scholar] [CrossRef] - Yousri, D.; Allam, D.; Eteiba, M.; Suganthan, P.N. Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants. Energy Convers. Manag.
**2019**, 182, 546–563. [Google Scholar] [CrossRef] - Li, S.; Gong, W.; Yan, X.; Hu, C.; Bai, D.; Wang, L.; Gao, L. Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization. Energy Convers. Manag.
**2019**, 186, 293–305. [Google Scholar] [CrossRef] - Chen, H.; Jiao, S.; Heidari, A.A.; Wang, M.; Chen, X.; Zhao, X. An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers. Manag.
**2019**, 195, 927–942. [Google Scholar] [CrossRef] - Chen, X.; Yue, H.; Yu, K. Perturbed stochastic fractal search for solar PV parameter estimation. Energy
**2019**, 189, 116247. [Google Scholar] [CrossRef] - Li, S.; Gong, W.; Yan, X.; Hu, C.; Bai, D.; Wang, L. Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy
**2019**, 190, 465–474. [Google Scholar] [CrossRef] - Pourmousa, N.; Ebrahimi, S.M.; Malekzadeh, M.; Alizadeh, M. Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm. Sol. Energy
**2019**, 180, 180–191. [Google Scholar] [CrossRef] - Ćalasan, M.; Jovanović, D.; Rubežić, V.; Mujović, S.; Đukanović, S. Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach. Energies
**2019**, 12, 4209. [Google Scholar] [CrossRef] - Chen, X.; Yu, K. Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters. Sol. Energy
**2019**, 180, 192–206. [Google Scholar] [CrossRef] - Kumar, C.; Raj, T.D.; Premkumar, M.; Raj, T.D. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik
**2020**, 223, 165277. [Google Scholar] [CrossRef] - Abd Elaziz, M.; Oliva, D. Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers. Manag.
**2018**, 171, 1843–1859. [Google Scholar] [CrossRef] - Merchaoui, M.; Sakly, A.; Mimouni, M.F. Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction. Energy Convers. Manag.
**2018**, 175, 151–163. [Google Scholar] [CrossRef] - Beigi, A.M.; Maroosi, A. Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms. Solar Energy
**2018**, 171, 435–446. [Google Scholar] [CrossRef] - Gao, X.; Cui, Y.; Hu, J.; Xu, G.; Wang, Z.; Qu, J.; Wang, H. Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Convers. Manag.
**2018**, 157, 460–479. [Google Scholar] [CrossRef] - Louzazni, M.; Khouya, A.; Amechnoue, K.; Gandelli, A.; Mussetta, M.; Crăciunescu, A. Metaheuristic algorithm for photovoltaic parameters: Comparative study and prediction with a firefly algorithm. Appl. Sci.
**2018**, 8, 339. [Google Scholar] [CrossRef] - Chen, X.; Xu, B.; Mei, C.; Ding, Y.; Li, K. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl. Energy
**2018**, 212, 1578–1588. [Google Scholar] [CrossRef] - Messaoud, R.B. Extraction of uncertain parameters of single-diode model of a photovoltaic panel using simulated annealing optimization. Energy Rep.
**2020**, 6, 350–357. [Google Scholar] [CrossRef] - Chin, V.J.; Salam, Z.; Ishaque, K. An accurate and fast computational algorithm for the two-diode model of PV module based on a hybrid method. IEEE Trans. Ind. Electron.
**2017**, 64, 6212–6222. [Google Scholar] [CrossRef] - Kang, T.; Yao, J.; Jin, M.; Yang, S.; Duong, T. A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies
**2018**, 11, 1060. [Google Scholar] [CrossRef] [Green Version] - Bendaoud, R.; Amiry, H.; Benhmida, M.; Zohal, B.; Yadir, S.; Bounouar, S.; Hajjaj, C.; Baghaz, E.; El Aydi, M. New method for extracting physical parameters of PV generators combining an implemented genetic algorithm and the simulated annealing algorithm. Sol. Energy
**2019**, 194, 239–247. [Google Scholar] [CrossRef] - Cárdenas, A.A.; Carrasco, M.; Mancilla-David, F.; Street, A.; Cárdenas, R. Experimental parameter extraction in the single-diode photovoltaic model via a reduced-space search. IEEE Trans. Ind. Electron.
**2016**, 64, 1468–1476. [Google Scholar] [CrossRef] - Ishibashi, K.i.; Kimura, Y.; Niwano, M. An extensively valid and stable method for derivation of all parameters of a solar cell from a single current-voltage characteristic. J. Appl. Phys.
**2008**, 103, 094507. [Google Scholar] [CrossRef] - Shaheen, A.M.; Spea, S.R.; Farrag, S.M.; Abido, M.A. A review of meta-heuristic algorithms for reactive power planning problem. Ain Shams Eng. J.
**2018**, 9, 215–231. [Google Scholar] [CrossRef] - Abd Elaziz, M.; Elsheikh, A.H.; Sharshir, S.W. Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system. Int. J. Refrig.
**2019**, 102, 47–54. [Google Scholar] [CrossRef] - Waly, H.M.; Azazi, H.Z.; Osheba, D.S.; El-Sabbe, A.E. Parameters extraction of photovoltaic sources based on experimental data. IET Renew. Power Gener.
**2019**, 13, 1466–1473. [Google Scholar] [CrossRef] - Chen, H.; Jiao, S.; Wang, M.; Heidari, A.A.; Zhao, X. Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. J. Clean. Prod.
**2020**, 244, 118778. [Google Scholar] [CrossRef] - Chen, H.; Heidari, A.A.; Chen, H.; Wang, M.; Pan, Z.; Gandomi, A.H. Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Gener. Comput. Syst.
**2020**, 111, 175–198. [Google Scholar] [CrossRef] - Naeijian, M.; Rahimnejad, A.; Ebrahimi, S.M.; Pourmousa, N.; Gadsden, S.A. Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm. Energy Rep.
**2021**, 7, 4047–4063. [Google Scholar] [CrossRef] - Yousri, D.; Thanikanti, S.B.; Allam, D.; Ramachandaramurthy, V.K.; Eteiba, M. Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters. Energy
**2020**, 195, 116979. [Google Scholar] [CrossRef] - Duman, S.; Kahraman, H.T.; Sonmez, Y.; Guvenc, U.; Kati, M.; Aras, S. A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Eng. Appl. Artif. Intell.
**2022**, 111, 104763. [Google Scholar] [CrossRef] - Chenche, L.E.P.; Mendoza, O.S.H.; Bandarra Filho, E.P. Comparison of four methods for parameter estimation of mono-and multi-junction photovoltaic devices using experimental data. Renew. Sustain. Energy Rev.
**2018**, 81, 2823–2838. [Google Scholar] [CrossRef] - Ibrahim, I.A.; Hossain, M.; Duck, B.C.; Nadarajah, M. An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model. Energy Convers. Manag.
**2020**, 213, 112872. [Google Scholar] [CrossRef] - Arandian, B.; Eslami, M.; Khalid, S.A.; Khan, B.; Sheikh, U.U.; Akbari, E.; Mohammed, A.H. An Effective Optimization Algorithm for Parameters Identification of Photovoltaic Models. IEEE Access
**2022**, 10, 34069–34084. [Google Scholar] [CrossRef] - Franco, R.; Vieira, F. Analytical method for extraction of the single-diode model parameters for photovoltaic panels from datasheet data. Electron. Lett.
**2018**, 54, 519–521. [Google Scholar] [CrossRef] - Haddad, S.; Lekouaghet, B.; Benghanem, M.; Soukkou, A.; Rabhi, A. Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using Artificial Hummingbird Algorithm. IEEE Access
**2022**, 10, 51299–51314. [Google Scholar] [CrossRef] - Jiao, S.; Chong, G.; Huang, C.; Hu, H.; Wang, M.; Heidari, A.A.; Chen, H.; Zhao, X. Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy
**2020**, 203, 117804. [Google Scholar] [CrossRef] - Li, S.; Gu, Q.; Gong, W.; Ning, B. An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models. Energy Convers. Manag.
**2020**, 205, 112443. [Google Scholar] [CrossRef] - Deotti, L.M.P.; Pereira, J.L.R.; da Silva Junior, I.C. Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm. Energy Convers. Manag.
**2020**, 221, 113114. [Google Scholar] [CrossRef] - Zhang, H.; Heidari, A.A.; Wang, M.; Zhang, L.; Chen, H.; Li, C. Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules. Energy Convers. Manag.
**2020**, 211, 112764. [Google Scholar] [CrossRef] - Ismaeel, A.A.; Houssein, E.H.; Oliva, D.; Said, M. Gradient-based optimizer for parameter extraction in photovoltaic models. IEEE Access
**2021**, 9, 13403–13416. [Google Scholar] [CrossRef] - Shaban, H.; Houssein, E.H.; Pérez-Cisneros, M.; Oliva, D.; Hassan, A.Y.; Ismaeel, A.A.; AbdElminaam, D.S.; Deb, S.; Said, M. Identification of parameters in photovoltaic models through a runge kutta optimizer. Mathematics
**2021**, 9, 2313. [Google Scholar] [CrossRef] - Xiong, G.; Li, L.; Mohamed, A.W.; Yuan, X.; Zhang, J. A new method for parameter extraction of solar photovoltaic models using gaining–sharing knowledge based algorithm. Energy Rep.
**2021**, 7, 3286–3301. [Google Scholar] [CrossRef] - Zhou, W.; Wang, P.; Heidari, A.A.; Zhao, X.; Turabieh, H.; Chen, H. Random learning gradient based optimization for efficient design of photovoltaic models. Energy Convers. Manag.
**2021**, 230, 113751. [Google Scholar] [CrossRef] - Zhou, W.; Wang, P.; Heidari, A.A.; Zhao, X.; Turabieh, H.; Mafarja, M.; Chen, H. Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules. Energy Rep.
**2021**, 7, 5175–5202. [Google Scholar] [CrossRef] - Abdel-Basset, M.; El-Shahat, D.; Chakrabortty, R.K.; Ryan, M. Parameter estimation of photovoltaic models using an improved marine predators algorithm. Energy Convers. Manag.
**2021**, 227, 113491. [Google Scholar] [CrossRef] - Liu, Y.; Chong, G.; Heidari, A.A.; Chen, H.; Liang, G.; Ye, X.; Cai, Z.; Wang, M. Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models. Energy Convers. Manag.
**2020**, 223, 113211. [Google Scholar] [CrossRef] - Liang, J.; Qiao, K.; Yu, K.; Ge, S.; Qu, B.; Xu, R.; Li, K. Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution. Sol. Energy
**2020**, 207, 336–346. [Google Scholar] [CrossRef] - Xiong, G.; Zhang, J.; Shi, D.; Zhu, L.; Yuan, X.; Tan, Z. Winner-leading competitive swarm optimizer with dynamic Gaussian mutation for parameter extraction of solar photovoltaic models. Energy Convers. Manag.
**2020**, 206, 112450. [Google Scholar] [CrossRef] - Liang, J.; Qiao, K.; Yuan, M.; Yu, K.; Qu, B.; Ge, S.; Li, Y.; Chen, G. Evolutionary multi-task optimization for parameters extraction of photovoltaic models. Energy Convers. Manag.
**2020**, 207, 112509. [Google Scholar] [CrossRef] - Long, W.; Wu, T.; Xu, M.; Tang, M.; Cai, S. Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm. Energy
**2021**, 229, 120750. [Google Scholar] [CrossRef] - Abdel-Basset, M.; Mohamed, R.; Chakrabortty, R.K.; Sallam, K.; Ryan, M.J. An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations. Energy Convers. Manag.
**2021**, 227, 113614. [Google Scholar] [CrossRef] - Khursheed, M.-U.-N.; Alghamdi, M.A.; Khan, M.F.N.; Khan, A.K.; Khan, I.; Ahmed, A.; Kiani, A.T.; Khan, M.A. PV model parameter estimation using modified FPA with dynamic switch probability and step size function. IEEE Access
**2021**, 9, 42027–42044. [Google Scholar] - Yang, X.S. Flower pollination algorithm for global optimization. In International Conference on Unconventional Computing and Natural Computation; Springer: Berlin, Germany, 2012; pp. 240–249. [Google Scholar]
- Jordan, D.C.; Marion, B.; Deline, C.; Barnes, T.; Bolinger, M. PV field reliability status—Analysis of 100 000 solar systems. Prog. Photovolt. Res. Appl.
**2020**, 28, 739–754. [Google Scholar] [CrossRef] - Hara, S.; Douzono, H.; Imamura, M.; Yoshioka, T. Estimation of Photovoltaic Cell Parameters Using Measurement Data of Photovoltaic Module String Currents and Voltages. IEEE J. Photovolt.
**2022**, 12, 540–545. [Google Scholar] [CrossRef] - Kohno, T.; Gokita, K.; Shitanishi, H.; Toyosaki, M.; Nakamura, T.; Morikawa, K.; Hatano, M. Fault-diagnosis architecture for large-scale photovoltaic power plants that does not require additional sensors. IEEE J. Photovolt.
**2019**, 9, 780–789. [Google Scholar] [CrossRef] - Harrou, F.; Saidi, A.; Sun, Y.; Khadraoui, S. Monitoring of photovoltaic systems using improved kernel-based learning schemes. IEEE J. Photovolt.
**2021**, 11, 806–818. [Google Scholar] [CrossRef] - Mansouri, M.M.; Hadjeri, S.; Brahami, M. New method of detection, identification, and elimination of photovoltaic system faults in real time based on the adaptive Neuro-fuzzy system. IEEE J. Photovolt.
**2021**, 11, 797–805. [Google Scholar] [CrossRef] - Mathew, D.; Ram, J.P.; Pillai, D.S.; Kim, Y.J.; Elangovan, D.; Laudani, A.; Mahmud, A. Parameter Estimation of Organic Photovoltaic Cells–A Three-Diode Approach Using Wind-Driven Optimization Algorithm. IEEE J. Photovolt.
**2021**, 12, 327–336. [Google Scholar] [CrossRef] - Huang, G.; Liang, Y.; Sun, X.; Xu, C.; Yu, F. Analyzing S-Shaped I–V characteristics of solar cells by solving three-diode lumped-parameter equivalent circuit model explicitly. Energy
**2020**, 212, 118702. [Google Scholar] [CrossRef] - Mathew, D.; Rani, C.; Kumar, M.R.; Wang, Y.; Binns, R.; Busawon, K. Wind-driven optimization technique for estimation of solar photovoltaic parameters. IEEE J. Photovolt.
**2017**, 8, 248–256. [Google Scholar] [CrossRef] - Restrepo-Cuestas, B.J.; Montano, J.; Ramos-Paja, C.A.; Trejos-Grisales, L.A.; Orozco-Gutierrez, M.L. Parameter estimation of the bishop photovoltaic model using a genetic algorithm. Appl. Sci.
**2022**, 12, 2927. [Google Scholar] [CrossRef] - Abido, M.; Khalid, M.S. Seven-parameter PV model estimation using Differential Evolution. Electr. Eng.
**2018**, 100, 971–981. [Google Scholar] [CrossRef] - Kiani, A.T.; Nadeem, M.F.; Ahmed, A.; Sajjad, I.A.; Haris, M.S.; Martirano, L. Optimal parameter estimation of solar cell using simulated annealing inertia weight particle swarm optimization (SAIW-PSO). In Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Madrid, Spain, 9–12 June 2020; pp. 1–6. [Google Scholar]
- Shankar, N.; Saravanakumar, N.; Kumar, C.; Kamatchi Kannan, V.; Indu Rani, B. Opposition-based equilibrium optimizer algorithm for identification of equivalent circuit parameters of various photovoltaic models. J. Comput. Electron.
**2021**, 20, 1560–1587. [Google Scholar] [CrossRef] - Khursheed, M.-U.-N.; Nadeem, M.F.; Khalil, A.; Sajjad, I.; Raza, A.; Iqbal, M.Q.; Bo, R.; ur Rehman, W. Review of flower pollination algorithm: Applications and variants. In Proceedings of the 2020 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 22–23 February 2020; pp. 1–6.
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Awadallah, M.A.; Yang, X.S. Variants of the flower pollination algorithm: A review. Nat.-Inspired Algorithms Appl. Optim.
**2018**, 744, 91–118. [Google Scholar] - Niu, P.; Li, J.; Chang, L.; Zhang, X.; Wang, R.; Li, G. A novel flower pollination algorithm for modeling the boiler thermal efficiency. Neural Process. Lett.
**2019**, 49, 737–759. [Google Scholar] [CrossRef] - Alshammari, N.; Asumadu, J. Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms. Sustain. Cities Soc.
**2020**, 60, 102255. [Google Scholar] [CrossRef] - Maleki, A.; Nazari, M.A.; Pourfayaz, F. Harmony search optimization for optimum sizing of hybrid solar schemes based on battery storage unit. Energy Rep.
**2020**, 6, 102–111. [Google Scholar] [CrossRef] - Huynh, D.C.; Ho, L.D.; Dunnigan, M.W. Parameter estimation of solar photovoltaic cells using an improved artificial bee colony algorithm. In International Conference on Green Technology and Sustainable Development; Springer: Berlin, Germany, 2020; pp. 281–292. [Google Scholar]
- Tefek, M.F. Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems. J. Comput. Electron.
**2021**, 20, 2530–2562. [Google Scholar] [CrossRef] - Kiani, A.T.; Nadeem, M.F.; Ahmed, A.; Sajjad, I.A.; Raza, A.; Khan, I.A. Chaotic inertia weight particle swarm optimization (CIWPSO): An efficient technique for solar cell parameter estimation. In Proceedings of the 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 29–30 January 2020; pp. 1–6. [Google Scholar]
- Rezk, H.; Arfaoui, J.; Gomaa, M.R. Optimal parameter estimation of solar PV panel based on hybrid particle swarm and grey wolf optimization algorithms. Int. J. Interact. Multimed. Artif. Intell.
**2021**, in press. [Google Scholar] [CrossRef] - Bisht, R.; Sikander, A. A New Soft Computing-Based Parameter Estimation of Solar Photovoltaic System. Arab. J. Sci. Eng.
**2022**, 47, 3341–3353. [Google Scholar] [CrossRef] - Hao, Q.; Zhou, Z.; Wei, Z.; Chen, G. Parameters Identification of Photovoltaic Models Using a Multi-Strategy Success-History-Based Adaptive Differential Evolution. IEEE Access
**2020**, 8, 35979–35994. [Google Scholar] [CrossRef] - Laudani, A.; Fulginei, F.R.; Salvini, A. High performing extraction procedure for the one-diode model of a photovoltaic panel from experimental I–V curves by using reduced forms. Sol. Energy
**2014**, 103, 316–326. [Google Scholar] [CrossRef] - Alwan, N.T.; Majeed, M.H.; Shcheklein, S.E.; Ali, O.M.; PraveenKumar, S. Experimental study of a tilt single slope solar still integrated with aluminum condensate plate. Inventions
**2021**, 6, 77. [Google Scholar] [CrossRef] - Praveenkumar, S.; Gulakhmadov, A.; Agyekum, E.B.; T Alwan, N.; Velkin, V.I.; Sharipov, P.; Safaraliev, M.; Chen, X. Experimental Study on Performance Enhancement of a Photovoltaic Module Incorporated with CPU Heat Pipe—A 5E Analysis. Sensors
**2022**, 22, 6367. [Google Scholar] [CrossRef] - Chavan, S.V.; Devaprakasam, D. Improving the performance of solar photovoltaic thermal system using phase change material. Mater. Today Proc.
**2021**, 46, 5036–5041. [Google Scholar] [CrossRef] - Nada, S.; El-Nagar, D.; Hussein, H. Improving the thermal regulation and efficiency enhancement of PCM-Integrated PV modules using nano particles. Energy Convers. Manag.
**2018**, 166, 735–743. [Google Scholar] [CrossRef] - Idoko, L.; Anaya-Lara, O.; McDonald, A. Enhancing PV modules efficiency and power output using multi-concept cooling technique. Energy Rep.
**2018**, 4, 357–369. [Google Scholar] [CrossRef] - Deokar, V.H.; Bindu, R.S.; Potdar, S. Active cooling system for efficiency improvement of PV panel and utilization of waste-recovered heat for hygienic drying of onion flakes. J. Mater. Sci. Mater. Electron.
**2021**, 32, 2088–2102. [Google Scholar] [CrossRef] - PraveenKumar, S.; Agyekum, E.B.; Velkin, V.I.; Yaqoob, S.J.; Adebayo, T.S. Thermal management of solar photovoltaic module to enhance output performance: An experimental passive cooling approach using discontinuous aluminum heat sink. Int. J. Renew. Energy Res. (IJRER)
**2021**, 11, 1700–1712. [Google Scholar] - Abdallah, S.R.; Saidani-Scott, H.; Benedi, J. Experimental study for thermal regulation of photovoltaic panels using saturated zeolite with water. Sol. Energy
**2019**, 188, 464–474. [Google Scholar] [CrossRef] - Wongwuttanasatian, T.; Sarikarin, T.; Suksri, A. Performance enhancement of a photovoltaic module by passive cooling using phase change material in a finned container heat sink. Sol. Energy
**2020**, 195, 47–53. [Google Scholar] [CrossRef] - Agyekum, E.B.; PraveenKumar, S.; Eliseev, A.; Velkin, V.I. Design and construction of a novel simple and low-cost test bench point-absorber wave energy converter emulator system. Inventions
**2021**, 6, 20. [Google Scholar] [CrossRef] - Agyekum, E.B.; PraveenKumar, S.; Alwan, N.T.; Velkin, V.I.; Adebayo, T.S. Experimental Study on Performance Enhancement of a Photovoltaic Module Using a Combination of Phase Change Material and Aluminum Fins—Exergy, Energy and Economic (3E) Analysis. Inventions
**2021**, 6, 69. [Google Scholar] [CrossRef] - Agyekum, E.B.; PraveenKumar, S.; Alwan, N.T.; Velkin, V.I.; Shcheklein, S.E.; Yaqoob, S.J. Experimental investigation of the effect of a combination of active and passive cooling mechanism on the thermal characteristics and efficiency of solar PV module. Inventions
**2021**, 6, 63. [Google Scholar] [CrossRef] - Agyekum, E.B.; Adebayo, T.S.; Bekun, F.V.; Kumar, N.M.; Panjwani, M.K. Effect of two different heat transfer fluids on the performance of solar tower csp by comparing recompression supercritical CO
_{2}and rankine power cycles, China. Energies**2021**, 14, 3426. [Google Scholar] [CrossRef]

**Figure 5.**V–P characteristics of 315 Wp PV module applying different algorithms (

**a**) at 25 $\xb0$C and (

**b**) 45 $\xb0$C.

**Figure 6.**(

**a**) V–P characteristics when temperature changes at 1000 W/m${}^{2}$ irradiance. (

**b**) V–I characteristic when irradiance changes at 25 $\xb0$C [15].

Literature | Year | Algorithm | ${\mathit{I}}_{\mathit{p}\mathit{h}}$ (A) | ${\mathit{I}}_{0}$($\mathsf{\mu}$A) | ${\mathit{R}}_{\mathit{s}\mathit{h}}$($\mathbf{\Omega}$) | ${\mathit{R}}_{\mathit{s}}$($\mathbf{\Omega}$) | n | No. of Steps | RMSE |
---|---|---|---|---|---|---|---|---|---|

[42] | 2019 | EA | 0.76080000 | 0.32230000 | 53.76340000 | 0.036400000 | 1.4837000000 | - | 1.0072 × 10${}^{-2}$ |

[43] | 2018 | NA | 0.76074014 | 0.31285196 | 55.90738000 | 0.036615485 | 1.477729500 | - | 7.7301 × 10${}^{-4}$ |

[44] | 2020 | EA | 0.76038466 | 0.23082625 | 53.67788300 | 0.037991668 | 1.447929015 | - | 9.7505 × 10${}^{-4}$ |

[45] | 2019 | EA | 0.76080000 | 0.32300000 | 53.71850000 | 0.036400000 | 1.481200000 | - | 9.8602 × 10${}^{-4}$ |

[46] | 2017 | EA | 0.76080000 | 0.32280000 | 53.75950000 | 0.036400000 | 1.481100000 | - | 9.8603 × 10${}^{-4}$ |

[47] | 2020 | EA | 0.76077600 | 0.32302100 | 53.71852000 | 0.036377000 | 1.481184000 | - | 9.8602 × 10${}^{-4}$ |

[48] | 2019 | NA + EA | 0.76078797 | 0.31068450 | 52.88979426 | 0.036546950 | 1.477267780 | - | 7.7301 × 10${}^{-4}$ |

[49] | 2019 | EA | 0.76077552 | 0.32302000 | 53.71852000 | 0.036370000 | 1.481108170 | - | 9.8602 × 10${}^{-4}$ |

[50] | 2019 | EA | 0.76079000 | 0.31062000 | 52.88500000 | 0.036548000 | 1.477100000 | - | 7.7300 × 10${}^{-4}$ |

[51] | 2019 | EA | 0.76080000 | 0.32300000 | 53.71850000 | 0.036400000 | 1.481200000 | - | 9.8602 × 10${}^{-4}$ |

[52] | 2019 | EA | 0.76077562 | 0.32301700 | 53.71821748 | 0.036377160 | 1.481182200 | - | 9.8602 × 10${}^{-4}$ |

[53] | 2019 | EA | 0.76078000 | 0.32302000 | 53.71852000 | 0.036380000 | 1.481180000 | - | 9.8602 × 10${}^{-4}$ |

[54] | 2019 | EA | 0.76080000 | 0.32300000 | 53.71850000 | 0.036400000 | 1.481200000 | - | 9.8602 × 10${}^{-4}$ |

[55] | 2019 | EA | 0.76077500 | 0.32302100 | 53.71867900 | 0.036377000 | 1.481108000 | - | 9.8602 × 10${}^{-4}$ |

[56] | 2019 | EA | 0.76077450 | 0.32300180 | 53.73000000 | 0.036377500 | 1.481177400 | - | 9.8602 × 10${}^{-4}$ |

[57] | 2019 | EA | 0.76078000 | 0.32302000 | 53.71852000 | 0.036380000 | 1.481180000 | - | 9.8602 × 10${}^{-4}$ |

[58] | 2020 | EA | 0.76076000 | 0.32314000 | 53.71489000 | 0.036370000 | 1.481140000 | - | 9.8482 × 10${}^{-4}$ |

[59] | 2018 | EA | 0.76077000 | 0.32320000 | 53.68360000 | 0.036300000 | 1.520800000 | - | 9.8600 × 10${}^{-5}$ |

[60] | 2018 | EA | 0.76078700 | 0.31068300 | 52.88971000 | 0.036546000 | 1.475262000 | - | 7.7301 × 10${}^{-4}$ |

[61] | 2018 | EA | 0.76077700 | 0.32262200 | 53.67840000 | 0.036381900 | 1.481060000 | - | 9.8602 × 10${}^{-4}$ |

[62] | 2018 | EA | 0.76077553 | 0.32302083 | 53.71852771 | 0.036377090 | 1.481183600 | - | 9.8602 × 10${}^{-4}$ |

[63] | 2018 | EA | 0.76069712 | 0.43244110 | 53.40180803 | 0.033410590 | 1.452456660 | - | 5.1382 × 10${}^{-4}$ |

[64] | 2018 | EA | 0.76078000 | 0.32302000 | 53.71636000 | 0.036380000 | 1.481180000 | - | 9.8602 × 10${}^{-4}$ |

[65] | 2020 | NA | 0.76870000 | 9.9414E-07 | 100.0000000 | 0.030966000 | 1.602000000 | 26 | 2.7756 × 10${}^{-17}$ |

[66] | 2017 | NA | 0.76072000 | 0.31911000 | 54.19241000 | 0.036290000 | 1.479860000 | - | 8.1291 × 10${}^{-4}$ |

[67] | 2018 | EA | 0.76077600 | 0.32302100 | 53.71852400 | 0.036377000 | 1.481718000 | - | 9.8602 × 10${}^{-4}$ |

[68] | 2019 | EA | 0.76078000 | 0.33971000 | 54.43370000 | 0.036160000 | 1.486290000 | - | 9.9185 × 10${}^{-4}$ |

Literature | Algorithm | ${\mathit{I}}_{\mathit{p}\mathit{h}}$ (A) | ${\mathit{I}}_{0}$($\mathsf{\mu}$A) | ${\mathit{R}}_{\mathit{s}\mathit{h}}$($\mathbf{\Omega}$) | ${\mathit{R}}_{\mathit{s}}$($\mathbf{\Omega}$) | n | No. of Steps | RMSE | MAE |
---|---|---|---|---|---|---|---|---|---|

[69] | AA + NA | 1.032377 | 2.517957 | 745.7122 | 1.239060 | 1.3173635 | 27 | 2.0465456 × 10${}^{-3}$ | 1.6925284 × 10${}^{-3}$ |

[43] | AA + NA | 1.0323823 | 2.5129059 | 744.71302 | 1.3001512 | 1.3171591 | 6 | 2.0465347 × 10${}^{-3}$ | 1.6923215 × 10${}^{-3}$ |

[70] | AA + NA | 1 | 2.3 | 830 | 1.3 | 1.3056 | - | 3.26 × 10${}^{-2}$ | - |

[6] | AA + NA | 1.033285 | 1.82 | 850.7068 | 1.357607 | 1.2857 | - | 5.181 × 10${}^{-3}$ | - |

[15] | AA + NA | 1.0323729 | 2.5129158 | 744.713061 | 1.2456174 | 1.3248753 | 4 | 2.046479 × 10${}^{-3}$ | 3.423077 × 10${}^{-4}$ |

[44] | EA | 1.0263 | 9.5710 | 6842.2 | 0.0298 | 1.5255 | - | 3.819492 × 10${}^{-3}$ | - |

[65] | NA | 1.0285 | 4.9614 × 10${}^{-6}$ | 1632.5 | 1.1638 | - | - | 2.6174×10${}^{-3}$ | - |

Parameter | Symbol | Value |
---|---|---|

Maximum power | ${P}_{MP}$ | 315 W |

Short-circuit current | ${I}_{SC}$ | 8.95 A |

Open-circuit voltage | ${V}_{OC}$ | 45.6 V |

Current at Maximum power | ${I}_{MP}$ | 8.45 A |

Voltage at Maximum power | ${V}_{MP}$ | 37.3 V |

Co-efficient of current | ${K}_{I}$ | 0.05%/$\xb0$C |

Co-efficient of voltage | ${K}_{V}$ | −0.35%/$\xb0$C |

Co-efficient of power | ${K}_{P}$ | −0.40%/$\xb0$C |

Nominal operating cell temperature | NOCT | 45 ± 2 $\xb0$C |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Changmai, P.; Deka, S.; Kumar, S.; Babu, T.S.; Aljafari, B.; Nastasi, B.
A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters. *Energies* **2022**, *15*, 7212.
https://doi.org/10.3390/en15197212

**AMA Style**

Changmai P, Deka S, Kumar S, Babu TS, Aljafari B, Nastasi B.
A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters. *Energies*. 2022; 15(19):7212.
https://doi.org/10.3390/en15197212

**Chicago/Turabian Style**

Changmai, Papul, Sunil Deka, Shashank Kumar, Thanikanti Sudhakar Babu, Belqasem Aljafari, and Benedetto Nastasi.
2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters" *Energies* 15, no. 19: 7212.
https://doi.org/10.3390/en15197212