BRIEF REVIEW ON SOLAR PHOTOVOLTAIC PARAMETER ESTIMATION OF SINGLE AND DOUBLE DIODE MODEL USING EVOLUTIONARY ALGORITHMS

Authors

  • S. Senthilkumar Assistant Professor, Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India https://orcid.org/0000-0002-1552-3475
  • V. Mohan Professor, Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
  • G. Krithiga Assistant Professor, Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India

DOI:

https://doi.org/10.29121/ijetmr.v10.i1.2023.1291

Keywords:

Renewable Energy, Photovoltaic Cell, Parameter Estimation, Single Diode And Double Diode Model, Evolutionary Algorithms

Abstract

In modern years several researchers contribute to renewable energy specifically solar as atmosphere responsive. Solar panels are an essential and chief constituent for solar energy selecting as they are active in transformation of solar radioactivity into electrical voltage correspondent. The principal concern in optimum generation of power from these solar panels is to be contingent on numerous characteristics mostly correlated to the sizing and modelling of photovoltaic (PV) panels for the essential presentations. An array of solar cells is castoff for generation of slight to average gauge power generation in numerous cases. Sizing of panels, the storing progression and application of electrical tracks in the procedure are some vital research qualities which together regulate and describe supreme power generation from solar panel. Parameter and circuit level modelling has been occupied as major problem of examination and several state of the art practices to govern the ideal sizing with respect to many circuit models such as single diode model (SDM) and double diode models (DDM) have been inspected in an widespread way in this review article. It offers the perceptions, features, and climaxes the strength and weaknesses of PV cell models. This article debates some algorithms and methods used in both SDM and DDM and a deep learning into the investigation of parameter assessments in each diode have been studied. Based on the showed evaluation, some commendations for upcoming research are provided.

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References

Abido, M. A., & Khalid, M. S. (2018). Seven-Parameter PV Model Estimation Using Differential Evolution. Electrical Engineering, 100(2), 971–981. https://doi.org/10.1007/s00202-017-0542-2. DOI: https://doi.org/10.1007/s00202-017-0542-2

Alam, D. F., Yousri, D. A., & Eteiba, M. B. (2015). Flower Pollination Algorithm Based Solar PV Parameter Estimation. Energy Conversion and Management, 101, 410–422. https://doi.org/10.1016/j.enconman.2015.05.074. DOI: https://doi.org/10.1016/j.enconman.2015.05.074

AlHajri, M. F., El-Naggar, K. M., AlRashidi, M. R., & Al-Othman, A. K. (2012). Optimal Extraction of Solar Cell Parameters Using Pattern Search. Renewable Energy, 44, 238–245. https://doi.org/10.1016/j.renene.2012.01.082. DOI: https://doi.org/10.1016/j.renene.2012.01.082

AlRashidi, M. R., El-Naggar, K. M., & Alhajri, M. F. (2013). Solar Cell Parameter Estimation Using Simulated Annealing Algorithm. International Journal of Electrical and Computer Engineering, 7(4), 370–373.

Araújo, K., Boucher, J. L., & Aphale, O. (2019). A Clean Energy Assessment of Early Adopters in Electric Vehicle and Solar Photovoltaic Technology : Geospatial, Political and Socio-Demographic Trends in New York. Journal of Cleaner Production, 216(10), 99–116. https://doi.org/10.1016/j.jclepro.2018.12.208. DOI: https://doi.org/10.1016/j.jclepro.2018.12.208

Askarzadeh, A., & Rezazadeh, A. (2012). Parameter Identification foe Solar Cell Models Using Harmonic Search-Based Algorithms. Solar Energy, 86(11), 3241–3249. https://doi.org/10.1016/j.solener.2012.08.018. DOI: https://doi.org/10.1016/j.solener.2012.08.018

Ayodele, T. R., Ogunjuyigbe, A. S. O., & Ekoh, E. E. (2016). Evaluation of Numerical Algorithms Used in Extracting the Parameters of a Single-Diode Photovoltaic Model. Sustainable Energy Technologies and Assessments, 13(1), 51–59. https://doi.org/10.1016/j.seta.2015.11.003. DOI: https://doi.org/10.1016/j.seta.2015.11.003

Bastidas-Rodriguez, J. D., Petrone, G., Ramos-Paja, C. A., & Spagnuolo, G. (2017). A Genetic Algorithm for Identifying the Single Diode Model Parameters of a Photovoltaic Panel. Mathematics and Computers in Simulation, 131(1), 38–54. https://doi.org/10.1016/j.matcom.2015.10.008. DOI: https://doi.org/10.1016/j.matcom.2015.10.008

Beigi, A. M., & Maroosi, A. (2018). Parameter Identification for Solar Cells and Module Using a Hybrid Firefly and Pattern Search Algorithms. Solar Energy, 171, 435–446. https://doi.org/10.1016/j.solener.2018.06.092. DOI: https://doi.org/10.1016/j.solener.2018.06.092

Ben Messaoud, R. (2020). Extraction of Uncertain Parameters of Single-Diode Model of a Photovoltaic Panel Using Simulated Annealing Optimization. Energy Reports, 6, 350–357. https://doi.org/10.1016/j.egyr.2020.01.016. DOI: https://doi.org/10.1016/j.egyr.2020.01.016

Bharatiraja, C., Jeevananthan, S., Latha, R., & Mohan, V. (2016). Vector Selection Approach-Based Hexagonal Hysteresis Space Vector Current Controller for a three Phase Diode Clamped MLI with Capacitor Voltage Balancing. IET Power Electronics, 9(7), 1350–1361. https://doi.org/10.1049/iet-pel.2015.0184. DOI: https://doi.org/10.1049/iet-pel.2015.0184

Brondani, M. D. F., Sausen, A. T. Z. R., Sausen, P. S., & Binelo, M. O. (2017). Battery Model Parameters Estimation Using Simulated Annealing. TEMA (São Carlos), 18(1), 127–137. https://doi.org/10.5540/tema.2017.018.01.0127. DOI: https://doi.org/10.5540/tema.2017.018.01.0127

Chen, X., Xu, B., Mei, C., Ding, Y., & Li, K. (2018). Teaching–Learning–Based Artificial Bee Colony for Solar Photovoltaic Parameter Estimation. Applied Energy, 212, 1578–1588. https://doi.org/10.1016/j.apenergy.2017.12.115. DOI: https://doi.org/10.1016/j.apenergy.2017.12.115

Chen, Y., Chen, Z., Wu, L., Long, C., Lin, P., & Cheng, S. (2019). Parameter Extraction of PV Models Using an Enhanced Shuffled Complex Evolution Algorithm Improved by Opposition-Based Learning. Energy Procedia, 158(1), 991–997. https://doi.org/10.1016/j.egypro.2019.01.242. DOI: https://doi.org/10.1016/j.egypro.2019.01.242

Chitrakala, G., Stalin, N., & Mohan, V. (2018). A Segmented Ladder Structured Multilevel Inverter for Switch Count Remission and Dual-Mode Savvy. Journal of Circuits, Systems, and Computers (JCSC), 27(14), (1-14). https://doi.org/10.1142/S0218126618502237. DOI: https://doi.org/10.1142/S0218126618502237

Chitrakala, G., Stalin, N., & Mohan, V. (2019). Normally Bypassed Cascaded Sources Multilevel Inverter with RGA Optimization for Reduced Output Distortion and Formulaic Passive Filter Design. Journal of Circuits, Systems and Computers, 29(02), (1-21). https://doi.org/10.1142/S021812662050019X. DOI: https://doi.org/10.1142/S021812662050019X

Choudhary, P., & Srivastava, R. K. (2019). Sustainability Perspectives-A Review for Solar Photovoltaic Trends and Growth Opportunities. Journal of Cleaner Production, 227(1), 589–612. https://doi.org/10.1016/j.jclepro.2019.04.107. DOI: https://doi.org/10.1016/j.jclepro.2019.04.107

De Groote, O., & Verboven, F. (2019). Subsidie and Time Discounting in New Technology Adoption : Evidence From Solar Photovoltaic Systems. American Economic Review, 109(6), 2137–2172. https://doi.org/10.1257/aer.20161343. DOI: https://doi.org/10.1257/aer.20161343

Derick, M., Rani, C., Rajesh, M., Busawon, K., & Binns, R. (2016, November). Estimation of Solar Photovoltaic Parameters Using Pattern Search Algorithm, International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering, Mauritius. DOI: https://doi.org/10.1007/978-3-319-52171-8_15

Hamid, N. F. A., Rahim, N. A., & Selvaraj, J. (2013, November). Solar Cell Parameters Extraction Using Particle Swarm Optimization Algorithm. International Conference on Clean Energy Technology (CEAT), Lankgkawi, Malaysia. https://doi.org/10.1109/CEAT.2013.6775676. DOI: https://doi.org/10.1109/CEAT.2013.6775676

Harrag, A., & Messalti, S. (2015, December). Extraction of Solar Cell Parameters Using Genetic Algorithm 4th International Conference on Electrical Engineering (ICEE), Boumerdès, Algeria. https://doi.org/10.1109/INTEE.2015.7416775. DOI: https://doi.org/10.1109/INTEE.2015.7416775

Hassan, S., Abdelmajid, B., Mourad, Z., Aicha, S., & Abdenaceur, B. (2017). An Advanced Mppt Based on Artificial Bee Colony Algorithm for Mppt Photovoltaic System Under Partial Shading Condition. International Journal of Power Electronics and Drive Systems, 8(2), 647–653. https://doi.org/10.11591/ijpeds.v8.i2.pp647-653. DOI: https://doi.org/10.11591/ijpeds.v8.i2.pp647-653

Ibrahim, H., & Anani, N. (2017). Evaluation of Analytical Methods for Parameter Extraction of PV Modules. Energy Procedia, 134(1), 69–78. https://doi.org/10.1016/j.egypro.2017.09.601. DOI: https://doi.org/10.1016/j.egypro.2017.09.601

Jacob, B., Balasubramaniyan, K., Babu, T. S., & Rajasekar, N. (2015, February). Parameter Extraction Solar PV Double Diode Model Using Artificial Immune System Ieee International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Kozhikode, India. DOI: https://doi.org/10.1109/SPICES.2015.7091390

Jadli, U., Thakur, P., & Shukla, R. D. (2018). A New Parameter Estimation Method of Solar Photovoltaic. IEEE Journal of Photovoltaics, 8(1), 239–247. https://doi.org/10.1109/JPHOTOV.2017.2767602. DOI: https://doi.org/10.1109/JPHOTOV.2017.2767602

Jamadi, M., Merrikh, F., & Bigdeli, M. (2015). Very Accurate Parameter Estimation of Single- and Double-Diode Solar Models Using A Modified Artificial Bee Colony Algorithm. International Journal of Energy and Environmental Engineering, 7, 13–25. https://doi.org/10.1007/s40095-015-0198-5. DOI: https://doi.org/10.1007/s40095-015-0198-5

Jervase, J. A., Bourdoucen, H., & Al-Lawati, A. (2001). Solar Cell Parameter Extraction Using Genetic Algorithms. Measurement Science and Technology. Institute of Physics Publishing, 12(11), 1922–1925. https://doi.org/10.1088/0957-0233/12/11/322. DOI: https://doi.org/10.1088/0957-0233/12/11/322

Kabeel, A. E., Abdelgaied, M., & Sathyamurthy, R. (2019). A Comprehensive Investigation of the Optimization Cooling Technique for Improving the Performance of PV Module with Reflectors Under Egyptian Conditions. Solar Energy, 186(1), 257–263. https://doi.org/10.1016/j.solener.2019.05.019. DOI: https://doi.org/10.1016/j.solener.2019.05.019

Khanna, V., Das, B. K., Bisht, D., & Singh, P. K. (2014). Estimation of Photovoltaic Cells Model Parameters Using Particle Swarm Optimization. Physics of Semiconductor Devices, Environmental Science and Engineering, 391–396. https://doi.org/10.1007/978-3-319-03002-9_98. DOI: https://doi.org/10.1007/978-3-319-03002-9_98

Krithiga, G., & Mohan, V. (2022). Elimination of Harmonics in Multilevel Inverter Using Multi-Group Marine Predator Algorithm-Based Enhanced RNN. International Transactions on Electrical Energy Systems, 1–13. https://doi.org/10.1155/2022/8004425. DOI: https://doi.org/10.1155/2022/8004425

Krithiga, G., Mohan, V., Chitrakala, G., & Senthilkumar, S. (2023). Optimization of Switching Angles for Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters Employing Artificial Intelligence Techniques – A Mini Review. International Journal of Engineering Technologies and Management Research, 10(1), 1–16. https://doi.org/10.29121/ijetmr.v10.i1.2023.1278. DOI: https://doi.org/10.29121/ijetmr.v10.i1.2023.1278

López-Guede, J. M., Ramos-Hernanz, J. A., & Graña, M. (2013, September). Artificial Neural Network Modeling of a Photovoltaic Module. International Joint Conference SOCO. In CISIS2013-ICEUTE2013, Salamanca, Spain, 11–13).

Meiying, Y., Wang, X., & Xu, Y. (2009). Parameter Extraction of Solar Cells Using Particle Swarm Optimization. Journal of Applied Physics, 105(9), 094502–094501 to 094502-8. https://doi.org/10.1063/1.3122082. DOI: https://doi.org/10.1063/1.3122082

Mohan, V., & Senthilkumar, S. (2022). IOT Based Fault Identification in Solar Photovoltaic Systems Using an Extreme Learning Machine Technique. Journal of Intelligent and Fuzzy Systems, 43(3), 3087–3100, 2022. https://doi.org/10.3233/JIFS-220012. DOI: https://doi.org/10.3233/JIFS-220012

Muangkote, N., Sunat, K., Chiewchanwattana, S., & Kaiwinit, S. (2019). An Advanced Onlooker-Ranking-Based Adaptive Differential Evolution to Extract the Parameters of Solar Cell Models. Renewable Energy, 134(1), 1129–1147. https://doi.org/10.1016/j.renene.2018.09.017. DOI: https://doi.org/10.1016/j.renene.2018.09.017

Nathangashree, D., Ramachandran, L., Senthilkumar, S., & Lakshmirekha, R. (2016). PLC Based Smart Monitoring System for Photovoltaic Panel Using Gsm Technology. International Journal of Advanced Research in Electronics and Communication Engineering, 5(2), 251–255.

Oliva, D., Cuevas, E., & Pajares, G. (2014). Parameter Identification Solar Cells Using Artificial Bee Colony Optimization. Energy, 72, 93–102. https://doi.org/10.1016/j.energy.2014.05.011. DOI: https://doi.org/10.1016/j.energy.2014.05.011

Pilakkat, D., & Kanthalakshmi, S. (2019). An Improved P&O Algorithm Integrated with Artificial Bee Colony for Photovoltaic Systems Under Partial Shading Conditions. Solar Energy, 178, 37–47. https://doi.org/10.1016/j.solener.2018.12.008. DOI: https://doi.org/10.1016/j.solener.2018.12.008

Rajasekar, N., Krishna Kumar, N. K., & Venugopalan, R. (2013). Bacterial Foraging Algorithm Based Solar PV Parameter Estimation. Solar Energy, 97, 255–265. https://doi.org/10.1016/j.solener.2013.08.019. DOI: https://doi.org/10.1016/j.solener.2013.08.019

Ram, J. P., Babu, T. S., Dragicevic, T., & Rajasekar, N. (2017). A New Hybrid Bee Pollinator Flower Pollination Algorithm for Solar PV Parameter Estimation. Energy Conversion and Management, 135(1), 463–476. https://doi.org/10.1016/j.enconman.2016.12.082. DOI: https://doi.org/10.1016/j.enconman.2016.12.082

Sahu, H. S., & Nayak, S. K. (2017). Estimation of Maximum Power Point of a Double Diode Model Photovoltaic Module. IET Power Electronics, 10(6), 667–675. https://doi.org/10.1049/iet-pel.2016.0632. DOI: https://doi.org/10.1049/iet-pel.2016.0632

Salmi, H., Badri, A., & Zegrari, M. (2016). Maximum Power Point Tracking (Mppt) Using Artificial Bee Colony Based Algorithm for Photovoltaic System. International Journal of Intelligent Information Systems, 5(1), 1–4. https://doi.org/10.11648/j.ijiis.20160501.11. DOI: https://doi.org/10.11648/j.ijiis.20160501.11

Satapathy, P., Dhar, S., & Dash, P. K. (2017). Stability Improvement of Pv-Bess Diesel Generator-Based Micro Grid with a New Modified Harmony Search-Based Hybrid Firefly Algorithm. IET Renewable Power Generation, 11(5), 566–577. https://doi.org/10.1049/iet-rpg.2016.0116. DOI: https://doi.org/10.1049/iet-rpg.2016.0116

Senthilkumar, S., Haidari, M., Devi, G., Sagai, A. S., Britto, F., Rajasekhar Gorthi, H., & Sivaramkrishnan, M. (2022). Wireless Bidirectional Power Transfer for E-vehicle Charging System. International Conference on Edge Computing and Applications (ICECAA), IEEE, 13-15 October 2022. https://doi.org/10.1109/ICECAA55415.2022.9936175. DOI: https://doi.org/10.1109/ICECAA55415.2022.9936175

Senthilkumar, S., Mohan, V., & Chitrakala, G. (2020). Evolutionary Algorithms for Solar Photovoltaic Parameters Estimation – A Review. International Journal of Future Generation Communication and Networking, 13(2), 348–360.

Senthilkumar, S., Mohan, V., Mangaiyarkarasi, S. P., & Karthikeyan, M. (2022). Analysis of Single-Diode PV Model and Optimized MPPT Model for Different Environmental Conditions. International Transactions on Electrical Energy Systems. https://doi.org/10.1155/2022/4980843. DOI: https://doi.org/10.1155/2022/4980843

Senthilkumar, S., Mohan, V., Senthil Kumar, T., Chitrakala, G., Ramachandran, L., & Devarajan, D. (2022). Solar Powered Pesticide Sprayer with Mobile Charger and Led Light. International Journal of Innovative Science and Research Technology, 7(4), 205–210. https://doi.org/10.5281/zenodo.6480781.

Sivamani, S., & Mohan, V. (2022). A Three-Phase Reduced Switch Count Multilevel Inverter Topology. International Transactions on Electrical Energy Systems, 2022, Article ID., 1–16. https://doi.org/10.1155/2022/6193731. DOI: https://doi.org/10.1155/2022/6193731

Sudhakar Babu, T., Prasanth Ram, J., Sangeetha, K., Laudani, A., & Rajasekar, N. (2016). Parameter Extraction of Two Diode Solar PV Model Using Fireworks Algorithm. Solar Energy, 140, 265–276. https://doi.org/10.1016/j.solener.2016.10.044. DOI: https://doi.org/10.1016/j.solener.2016.10.044

Suganya, S., Sinduja, R., Sowmiya, T., & Senthilkumar, S. (2014). Street Light Glow on Detecting Vechile Movement Using Sensor. International Journal for Advance Research in Engineering and Technology, ICIRET.

Tey, K. S., & Mekhilef, S. (2014). A Fast-Converging MPPT Technique for Photovoltaic System Under Fast Varying Solar Irradiation and Load Resistance. IEEE Transactions on Industrial Informatics, 11(1), 176–186. https://doi.org/10.1109/TII.2014.2378231. DOI: https://doi.org/10.1109/TII.2014.2378231

Waly, H. M., Azazi, H. Z., Osheba, D. S. M., & El-Sabbe, A. E. (2019). Parameters Extraction of Photovoltaic Sources Based on Experimental Data. IET Renewable Power Generation, 13(9), 1466–1473. https://doi.org/10.1049/iet-rpg.2018.5418. DOI: https://doi.org/10.1049/iet-rpg.2018.5418

Warkad, S. B., & Asole, R. R. (2019). Optimal Power Flow for Hybrid HVDC-AC Transmission System : A Genetic Algorithm Approach. International Journal of Future Generation Communication and Networking, 12(3), 01–13.

Xu, S., & Wang, Y. (2017). Parameter Estimation of Photovoltaic Modules Using Hybrid Flower Pollination Algorithm. Energy Conversion and Management, 144, 53–68. https://doi.org/10.1016/j.enconman.2017.04.042. DOI: https://doi.org/10.1016/j.enconman.2017.04.042

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Published

2023-01-26

How to Cite

Senthilkumar, S., V., M., & G., K. (2023). BRIEF REVIEW ON SOLAR PHOTOVOLTAIC PARAMETER ESTIMATION OF SINGLE AND DOUBLE DIODE MODEL USING EVOLUTIONARY ALGORITHMS. International Journal of Engineering Technologies and Management Research, 10(1), 64–78. https://doi.org/10.29121/ijetmr.v10.i1.2023.1291