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Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Yıl 2024, , 771 - 784, 15.07.2024
https://doi.org/10.34248/bsengineering.1490859

Öz

Recently, solar energy has become an attractive topic for researchers as it has been preferred among renewable energy sources due to its advantages such as unlimited energy supply and low maintenance expenses. The precise modeling of the solar cells and the model’s parameter estimate are two of the most important and difficult topics in photovoltaic systems. A solar cell’s behavior can be predicted based on its current-voltage characteristics and unknown model parameters. Therefore, many meta-heuristic search algorithms have been proposed in the literature to solve the PV parameter estimation problem. In this study, the enhanced crayfish optimization algorithm (ECOA) with opposition-based learning (OBL) strategies was proposed to estimate the parameters of the three different PV modules. A thorough simulation study was conducted to demonstrate the performance of the ECOA algorithm in tackling benchmark challenges and PV parameter estimate problems. In the first simulation study, using the three OBL strategies, six variations of the COA were created. The performances of these variations and the classic COA have been tested on CEC2020 benchmark problems. To determine the best COA variation, the results were analyzed using Friedman and Wilcoxon tests. In the second simulation study, the best variation, called ECOA, and the base COA were applied to estimate the parameters of three PV modules. According to the simulation results, the ECOA algorithm achieved 1.0880%, 37.8378%, and 0.8106% lower error values against the base COA for the parameter estimation of the STP6-120/36, Photowatt-PWP201, and STM6-40/36 PV modules. Moreover, the sensitivity analysis was performed in order to determine the parameters influencing the PV module’s performance. Accordingly, the change in the photo-generated current and diode ideality factor in the single-diode model affects the performance of PV modules the most. The comprehensive analysis and results showed the ECOA’s superior performance in parameter estimation of three PV modules compared to other algorithms found in the literature.

Kaynakça

  • Ali F, Sarwar, A, Bakhsh, FI, Ahmad, S, Shah, AA, Ahmed, H. 2023. Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation. Energy Convers Manag, 277: 116613.
  • Ayang A, Wamkeue R, Ouhrouche M, Djongyang N, Salomé NE, Pombe JK, Ekemb G. 2019. Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew Energy, 130: 111-121.
  • Ayyarao TS, Kishore GI. 2024. Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions. Soft Comput, 28(4): 3371-3392.
  • Cárdenas AA, Carrasco M, Mancilla-David F, Street A, Cardenas R. 2016. Experimental parameter extraction in the single-diode photovoltaic model via a reduced-space search. IEEE Trans Ind Electron, 64(2): 1468-1476.
  • Çetinbaş İ, Tamyurek B, Demirtaş M. 2023. Parameter extraction of photovoltaic cells and modules by hybrid white shark optimizer and artificial rabbits optimization. Energy Convers Manag, 296: 117621.
  • Çetinbaş İ. 2024. Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm. Glob Chall, 8(5): 2300355.
  • Chen X, Xu B, Mei C, Ding Y, Li K. 2018. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl Energy, 212: 1578-1588.
  • Chenche LEP, Mendoza OSH, Bandarra Filho EP. 2018. Comparison of four methods for parameter estimation of mono-and multi-junction photovoltaic devices using experimental data. Renew Sustain Energy Rev, 81: 2823-2838.
  • Duman S, Kahraman H.T, Sonmez Y, Guvenc U, Kati M, Aras S. 2022. A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Eng Appl Artif Intell, 111: 104763.
  • El-Dabah MA, El-Sehiemy RA, Hasanien HM, Saad B. 2023. Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm. Energy, 262: 125522.
  • El-Sehiemy R, Shaheen A, El-Fergany A, Ginidi A. 2023. Electrical parameters extraction of PV modules using artificial hummingbird optimizer. Sci Rep, 13(1): 9240.
  • Ergezer M, Simon D, Du D. 2009. Oppositional biogeography-based optimization. In: IEEE International Conference on Systems, Man and Cybernetics, October 11-14, San Antonio, TX, US, pp: 1009-1014.
  • Garip Z. 2023. Parameters estimation of three-diode photovoltaic model using fractional-order Harris Hawks optimization algorithm. Optik, 272: 170391.
  • Isen E, Duman S. 2024. Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems. Appl Energy, 365: 123297.
  • Izci D, Ekinci S, Hussien AG. 2024. Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm. Sci Rep, 14(1): 7945.
  • Jia H, Rao H, Wen C, Mirjalili S. 2023. Crayfish optimization algorithm. Artif Intell Rev, 56(2): 1919-1979.
  • Kumar C, Raj TD, Premkumar M, Raj TD. 2020. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik, 223: 165277.
  • Long W, Jiao J, Liang X, Xu M, Tang M, Cai S. 2022. Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm. Energy, 249: 123760.
  • Long W, Wu T, Jiao J, Tang M, Xu M. 2020. Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model. Eng Appl Artif Intell, 89: 103457.
  • Maden D, Çelik E, Houssein EH, Sharma G. 2023. Squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice. Neural Comput Appl, 35(18): 13529-13546.
  • Mahdavi S, Rahnamayan S, Deb K. 2018. Opposition based learning: A literature review. Swarm Evol Comput, 39: 1-23.
  • Naeijian M, Rahimnejad A, Ebrahimi SM, Pourmousa N, Gadsden SA. 2021. Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm. Energy Rep, 7: 4047-4063.
  • Navarro MA, Oliva D, Ramos-Michel A, Haro EH. 2023. An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models. Energy Convers Manag, 276: 116523.
  • Ortiz-Conde A, Sánchez FJG, Muci J. 2006. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Sol Energy Mater Sol Cells, 90(3): 352-361.
  • Premkumar M, Jangir P, Sowmya R, Elavarasan RM, Kumar BS. 2021. Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans, 116: 139-166.
  • Qais MH, Hasanien HM, Alghuwainem S. 2020. Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values. Energy Convers Manag, 214: 112904.
  • Qaraad M, Amjad S, Hussein NK, Badawy M, Mirjalili S, Elhosseini MA. 2023. Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators. Comput Electr Eng, 106: 108603.
  • Rahnamayan S, Tizhoosh HR, Salama MM. 2007. Quasi-oppositional differential evolution. In: IEEE Congress on Evolutionary Computation, September 25-28, Singapore, pp: 2229-2236.
  • Shaheen AM, Ginidi AR, El-Sehiemy RA, El-Fergany A, Elsayed AM. 2023. Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer. Energy, 283: 129034.
  • Sharma A, Sharma A, Averbukh M, Rajput S, Jately V, Choudhury S, Azzopardi B. 2022. Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. Energy Rep, 8: 6576-6592.
  • Tizhoosh HR. 2005. Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, November 28-30, Vienna, Austria, pp: 695-701.
  • Wang D, Sun X, Kang H, Shen Y, Chen Q. 2022. Heterogeneous differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Rep, 8: 4724-4746.
  • Wang J, Yang B, Li D, Zeng C, Chen Y, Guo Z, Yu T. 2021. Photovoltaic cell parameter estimation based on improved equilibrium optimizer algorithm. Energy Convers Manag, 236: 114051.
  • Wu L, Chen Z, Long C, Cheng S, Lin P, Chen Y, Chen H. 2018. Parameter extraction of photovoltaic models from measured IV characteristics curves using a hybrid trust-region reflective algorithm. Appl Energy, 232: 36-53.
  • Xiong G, Gu Z, Mohamed AW, Bouchekara HR, Suganthan PN. (2024). Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm. Inf Sci, 670, 120627.
  • Yang B, Wang J, Zhang X, Yu T, Yao W, Shu H, Sun L. 2020. Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification. Energy Convers Manag, 208:112595.
  • Yang C, Su C, Hu H, Habibi M, Safarpour H, Khadimallah MA. 2023. Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm. Sol Energy, 253: 343-359.
  • Yu S, Heidari AA, Liang G, Chen C, Chen H, Shao Q. 2022. Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization. Optik, 252: 168513.
  • Yu X, Hu Z, Wang X, Luo W. 2023. Ranking teaching–learning-based optimization algorithm to estimate the parameters of solar models. Eng Appl Artif Intell, 123: 106225.
  • Yue CT, Price KV, Suganthan PN, Liang JJ, Ali MZ, Qu BY, Biswas PP. 2019. Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization. Comput. Intell. Lab., Zhengzhou Univ., Technical Report, 201911, Henan, China, pp: 65.

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Yıl 2024, , 771 - 784, 15.07.2024
https://doi.org/10.34248/bsengineering.1490859

Öz

Recently, solar energy has become an attractive topic for researchers as it has been preferred among renewable energy sources due to its advantages such as unlimited energy supply and low maintenance expenses. The precise modeling of the solar cells and the model’s parameter estimate are two of the most important and difficult topics in photovoltaic systems. A solar cell’s behavior can be predicted based on its current-voltage characteristics and unknown model parameters. Therefore, many meta-heuristic search algorithms have been proposed in the literature to solve the PV parameter estimation problem. In this study, the enhanced crayfish optimization algorithm (ECOA) with opposition-based learning (OBL) strategies was proposed to estimate the parameters of the three different PV modules. A thorough simulation study was conducted to demonstrate the performance of the ECOA algorithm in tackling benchmark challenges and PV parameter estimate problems. In the first simulation study, using the three OBL strategies, six variations of the COA were created. The performances of these variations and the classic COA have been tested on CEC2020 benchmark problems. To determine the best COA variation, the results were analyzed using Friedman and Wilcoxon tests. In the second simulation study, the best variation, called ECOA, and the base COA were applied to estimate the parameters of three PV modules. According to the simulation results, the ECOA algorithm achieved 1.0880%, 37.8378%, and 0.8106% lower error values against the base COA for the parameter estimation of the STP6-120/36, Photowatt-PWP201, and STM6-40/36 PV modules. Moreover, the sensitivity analysis was performed in order to determine the parameters influencing the PV module’s performance. Accordingly, the change in the photo-generated current and diode ideality factor in the single-diode model affects the performance of PV modules the most. The comprehensive analysis and results showed the ECOA’s superior performance in parameter estimation of three PV modules compared to other algorithms found in the literature.

Kaynakça

  • Ali F, Sarwar, A, Bakhsh, FI, Ahmad, S, Shah, AA, Ahmed, H. 2023. Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation. Energy Convers Manag, 277: 116613.
  • Ayang A, Wamkeue R, Ouhrouche M, Djongyang N, Salomé NE, Pombe JK, Ekemb G. 2019. Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew Energy, 130: 111-121.
  • Ayyarao TS, Kishore GI. 2024. Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions. Soft Comput, 28(4): 3371-3392.
  • Cárdenas AA, Carrasco M, Mancilla-David F, Street A, Cardenas R. 2016. Experimental parameter extraction in the single-diode photovoltaic model via a reduced-space search. IEEE Trans Ind Electron, 64(2): 1468-1476.
  • Çetinbaş İ, Tamyurek B, Demirtaş M. 2023. Parameter extraction of photovoltaic cells and modules by hybrid white shark optimizer and artificial rabbits optimization. Energy Convers Manag, 296: 117621.
  • Çetinbaş İ. 2024. Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm. Glob Chall, 8(5): 2300355.
  • Chen X, Xu B, Mei C, Ding Y, Li K. 2018. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl Energy, 212: 1578-1588.
  • Chenche LEP, Mendoza OSH, Bandarra Filho EP. 2018. Comparison of four methods for parameter estimation of mono-and multi-junction photovoltaic devices using experimental data. Renew Sustain Energy Rev, 81: 2823-2838.
  • Duman S, Kahraman H.T, Sonmez Y, Guvenc U, Kati M, Aras S. 2022. A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Eng Appl Artif Intell, 111: 104763.
  • El-Dabah MA, El-Sehiemy RA, Hasanien HM, Saad B. 2023. Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm. Energy, 262: 125522.
  • El-Sehiemy R, Shaheen A, El-Fergany A, Ginidi A. 2023. Electrical parameters extraction of PV modules using artificial hummingbird optimizer. Sci Rep, 13(1): 9240.
  • Ergezer M, Simon D, Du D. 2009. Oppositional biogeography-based optimization. In: IEEE International Conference on Systems, Man and Cybernetics, October 11-14, San Antonio, TX, US, pp: 1009-1014.
  • Garip Z. 2023. Parameters estimation of three-diode photovoltaic model using fractional-order Harris Hawks optimization algorithm. Optik, 272: 170391.
  • Isen E, Duman S. 2024. Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems. Appl Energy, 365: 123297.
  • Izci D, Ekinci S, Hussien AG. 2024. Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm. Sci Rep, 14(1): 7945.
  • Jia H, Rao H, Wen C, Mirjalili S. 2023. Crayfish optimization algorithm. Artif Intell Rev, 56(2): 1919-1979.
  • Kumar C, Raj TD, Premkumar M, Raj TD. 2020. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik, 223: 165277.
  • Long W, Jiao J, Liang X, Xu M, Tang M, Cai S. 2022. Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm. Energy, 249: 123760.
  • Long W, Wu T, Jiao J, Tang M, Xu M. 2020. Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model. Eng Appl Artif Intell, 89: 103457.
  • Maden D, Çelik E, Houssein EH, Sharma G. 2023. Squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice. Neural Comput Appl, 35(18): 13529-13546.
  • Mahdavi S, Rahnamayan S, Deb K. 2018. Opposition based learning: A literature review. Swarm Evol Comput, 39: 1-23.
  • Naeijian M, Rahimnejad A, Ebrahimi SM, Pourmousa N, Gadsden SA. 2021. Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm. Energy Rep, 7: 4047-4063.
  • Navarro MA, Oliva D, Ramos-Michel A, Haro EH. 2023. An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models. Energy Convers Manag, 276: 116523.
  • Ortiz-Conde A, Sánchez FJG, Muci J. 2006. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Sol Energy Mater Sol Cells, 90(3): 352-361.
  • Premkumar M, Jangir P, Sowmya R, Elavarasan RM, Kumar BS. 2021. Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans, 116: 139-166.
  • Qais MH, Hasanien HM, Alghuwainem S. 2020. Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values. Energy Convers Manag, 214: 112904.
  • Qaraad M, Amjad S, Hussein NK, Badawy M, Mirjalili S, Elhosseini MA. 2023. Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators. Comput Electr Eng, 106: 108603.
  • Rahnamayan S, Tizhoosh HR, Salama MM. 2007. Quasi-oppositional differential evolution. In: IEEE Congress on Evolutionary Computation, September 25-28, Singapore, pp: 2229-2236.
  • Shaheen AM, Ginidi AR, El-Sehiemy RA, El-Fergany A, Elsayed AM. 2023. Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer. Energy, 283: 129034.
  • Sharma A, Sharma A, Averbukh M, Rajput S, Jately V, Choudhury S, Azzopardi B. 2022. Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. Energy Rep, 8: 6576-6592.
  • Tizhoosh HR. 2005. Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, November 28-30, Vienna, Austria, pp: 695-701.
  • Wang D, Sun X, Kang H, Shen Y, Chen Q. 2022. Heterogeneous differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Rep, 8: 4724-4746.
  • Wang J, Yang B, Li D, Zeng C, Chen Y, Guo Z, Yu T. 2021. Photovoltaic cell parameter estimation based on improved equilibrium optimizer algorithm. Energy Convers Manag, 236: 114051.
  • Wu L, Chen Z, Long C, Cheng S, Lin P, Chen Y, Chen H. 2018. Parameter extraction of photovoltaic models from measured IV characteristics curves using a hybrid trust-region reflective algorithm. Appl Energy, 232: 36-53.
  • Xiong G, Gu Z, Mohamed AW, Bouchekara HR, Suganthan PN. (2024). Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm. Inf Sci, 670, 120627.
  • Yang B, Wang J, Zhang X, Yu T, Yao W, Shu H, Sun L. 2020. Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification. Energy Convers Manag, 208:112595.
  • Yang C, Su C, Hu H, Habibi M, Safarpour H, Khadimallah MA. 2023. Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm. Sol Energy, 253: 343-359.
  • Yu S, Heidari AA, Liang G, Chen C, Chen H, Shao Q. 2022. Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization. Optik, 252: 168513.
  • Yu X, Hu Z, Wang X, Luo W. 2023. Ranking teaching–learning-based optimization algorithm to estimate the parameters of solar models. Eng Appl Artif Intell, 123: 106225.
  • Yue CT, Price KV, Suganthan PN, Liang JJ, Ali MZ, Qu BY, Biswas PP. 2019. Problem definitions and evaluation criteria for the CEC 2020 special session and competition on single objective bound constrained numerical optimization. Comput. Intell. Lab., Zhengzhou Univ., Technical Report, 201911, Henan, China, pp: 65.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotovoltaik Güç Sistemleri
Bölüm Research Articles
Yazarlar

Burçin Özkaya 0000-0002-9858-3982

Yayımlanma Tarihi 15 Temmuz 2024
Gönderilme Tarihi 27 Mayıs 2024
Kabul Tarihi 12 Temmuz 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Özkaya, B. (2024). Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies. Black Sea Journal of Engineering and Science, 7(4), 771-784. https://doi.org/10.34248/bsengineering.1490859
AMA Özkaya B. Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies. BSJ Eng. Sci. Temmuz 2024;7(4):771-784. doi:10.34248/bsengineering.1490859
Chicago Özkaya, Burçin. “Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm With Opposition-Based Learning Strategies”. Black Sea Journal of Engineering and Science 7, sy. 4 (Temmuz 2024): 771-84. https://doi.org/10.34248/bsengineering.1490859.
EndNote Özkaya B (01 Temmuz 2024) Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies. Black Sea Journal of Engineering and Science 7 4 771–784.
IEEE B. Özkaya, “Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies”, BSJ Eng. Sci., c. 7, sy. 4, ss. 771–784, 2024, doi: 10.34248/bsengineering.1490859.
ISNAD Özkaya, Burçin. “Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm With Opposition-Based Learning Strategies”. Black Sea Journal of Engineering and Science 7/4 (Temmuz 2024), 771-784. https://doi.org/10.34248/bsengineering.1490859.
JAMA Özkaya B. Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies. BSJ Eng. Sci. 2024;7:771–784.
MLA Özkaya, Burçin. “Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm With Opposition-Based Learning Strategies”. Black Sea Journal of Engineering and Science, c. 7, sy. 4, 2024, ss. 771-84, doi:10.34248/bsengineering.1490859.
Vancouver Özkaya B. Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies. BSJ Eng. Sci. 2024;7(4):771-84.

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