Open Access

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems


Cite

Abo-Elyousr, F. K., Abdelshafy, A. M. and Abdelaziz, A. Y. (2019). MPPT-based particle swarm and cuckoo search algorithms for PV systems. In: Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, pp. 379–400. doi: 10.1007/978-3-030-05578-314. Search in Google Scholar

Ahmed, J. and Salam, Z. (2015). A Critical Evaluation on Maximum Power Point Tracking Methods for Partial Shading in PV Systems. Renewable and Sustainable Energy Reviews, 47, pp. 933–953. doi: 10.1016/j.rser.2015.03.080. Search in Google Scholar

Ahmad, R., Murtaza, A. F., and Sher, H. A. (2019). Power tracking techniques for efficient operation of photovoltaic array in solar applications – a review, Renewable and Sustainable Energy Reviews, 101, 82–102. doi :10.1016/j.rser.2018.10.015 Search in Google Scholar

Baba, A. O., Liu, G. and Chen, X. (2020). Classification and Evaluation Review of Maximum Power Point Tracking Methods. Sustainable Futures, 2, p. 100020. doi: 10.1016/j.sftr.2020.100020. Search in Google Scholar

Bettahar, F., Sabrina, A. and Achour, B. (2023). Enhancing PV Systems with Intelligent MPPT and Improved Control Strategy of Z-Source Inverter. Power Electronics and Drives, 9(1), pp. 1–20. doi: 10.2478/pead-2024-0001. Search in Google Scholar

Bhukya, M. N. and Kota, V. R. (2018). A Novel P&OT-Neville’s Interpolation MPPT Scheme for Maximum PV System Energy Extraction. International Journal of Renewable Energy Development, 7(3), pp. 251–260. doi: 10.14710/ijred.7.3.251-260. Search in Google Scholar

Bollipo, R. B., Mikkili, S. and Bonthagorla, P. K. (2020). Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation. IET Renewable Power Generation, 14, pp. 1433–1452. doi: 10.1049/ietrpg.2019.1163. Search in Google Scholar

Chauhan, U., Rani, A., Kumar, B. and Singh, V. (2019). A Multi Verse Optimization based MPPT Controller for Drift Avoidance in Solar System. Intelligent & Fuzzy Systems, 36(3), pp. 2175–2184. doi: 10.3233/JIFS-169929. Search in Google Scholar

Chellal, M., Guimarães, T. F. and Leite, V. (2021). Experimental Evaluation of MPPT algorithms: A Comparative Study. International Journal of Renewable Energy Research, 11(1). doi: 10.20508/ijrer.v11i1.11797.g8164. Search in Google Scholar

Elmanakhly, D. A., Saleh, M., Rashed, E. A. and Abdel-Basset, M. (2022). BinHOA: Efficient Binary Horse Herd Optimization Method for Feature Selection: Analysis and Validations. IEEE Access, 10, pp. 26795–26816. doi: 10.1109/access.2022.3156593. Search in Google Scholar

Hanafiah, S., Ayad, A., Hehn, A. and Kennel, R. (2017).”A hybrid MPPT for quasi-Z-source inverters in PV applications under partial shading condition,” 2017 11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), Cadiz, Spain, 2017, pp. 418-423, doi: 10.1109/CPE.2017.7915208. Search in Google Scholar

Hota, S., Sahu, M. K. and Malla, J. M. R. (2017). A Standalone PV System with a Hybrid P&O MPPT Optimization Technique, Engineering. Technology & Applied Science Research, 7(6), pp. 2109–2112. doi: 10.48084/etasr.1374. Search in Google Scholar

Jately, V., Azzopardi, B., Joshi, J., Sharma, A. and Arora, S. (2021). Experimental Analysis of Hill-Climbing MPPT Algorithms under Low Irradiance Levels. Renewable and Sustainable Energy Reviews, 150, p. 111467. doi: 10.1016/j.rser.2021.111467. Search in Google Scholar

Khan, K., Rashid, S., Mansoor, M., Khan, A., Raza, H., Zafar, M. H. and Akhtar, N. (2023). Data-Driven Green Energy Extraction: Machine Learning-Based MPPT Control with Efficient Fault Detection Method for the Hybrid PV-TEG System. Energy Reports, 9, pp. 3604–3623. doi: 10.1016/j.egyr.2023.02.047. Search in Google Scholar

Kouser, S., Dheep, R. and Bansal, R. C. (2022). Horse herd optimization MPPT for grid connected PV system under partial shading conditions. In: IEEE 10th Power India International Conference (PIICON), New Delhi, India, pp. 1–6. Search in Google Scholar

Krishna, K. S. and Kumar, K. S. (2015). A Review on Hybrid Energy Systems. Renewable and Sustainable Energy Reviews, 52, pp. 907–916. doi: 10.1016/j.rser.2015.070187. Search in Google Scholar

Kumar, C. S. and Rao, R. S. (2016). A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. International Journal of Renewable Energy Development, 5(3), pp. 225–232. doi: 10.14710/ijred.5.3.225-232. Search in Google Scholar

Li, D., Zhou, H., Zhou, Y., Rao, Y. and Yao, W. (2023). Atom Search Optimization-based PV Array Reconfiguration Technique under Partial Shading Condition. International Transactions on Electrical Energy Systems, pp. 1–15. doi: 10.1155/2023/8685976. Search in Google Scholar

Li, H., Yang, D., Su, W., Lu, J. and Yu, X. (2019). An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System under Partial Shading. IEEE Transactions on Industrial Electronics, 66(1), pp. 265–275. doi: 10.1109/tie.2018.2829668. Search in Google Scholar

Memaya, M. B., Moorthy, C. B., Tahiliani, S. and Sreeni, S. (2019). Machine Learning Based Maximum Power Point Tracking in Solar Energy Conversion Systems. International Journal of Smart Grid and Clean Energy, 8(6), pp. 662–669. doi: 10.12720/sgce.8.6.662-669. Search in Google Scholar

Miar Naeimi, F., Azizyan, G. and Rashki, M. (2021). Horse Herd Optimization Algorithm: A Nature-Inspired Algorithm for High-Dimensional Optimization Problems. Knowledge Based Systems, 213, p. 106711. doi: 10.1016/j.knosys.2020.106711. Search in Google Scholar

Mirjalili, S., Mirjalili, S. M. and Lewis, A. (2014). A Grey Wolf Optimizer. Advances in Engineering Software, 69, pp. 46–61. doi: 10.1016/j.advengsoft.2013.12.007. Search in Google Scholar

Mohanty, S., Subudhi, B. and Ray, P. K. (2016). A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System under Partial Shading Conditions. IEEE Transactions on Sustainable Energy, 7(1), pp. 181–188. doi: 10.1109/TSTE.2015.2482120. Search in Google Scholar

Motahhir, S., El Hammoumi, A. and El Ghzizal, A. (2018). Photovoltaic System with Quantitative Comparative between an Improved MPPT and Existing INC and P&O Methods Under Fast Varying of Solar Irradiation. Energy Reports, 4, pp. 341–350. doi: 10.1016/j.egyr.2018.04.003. Search in Google Scholar

Naick, B. K., Chatterjee, T. K. and Chatterjee, K. (2017). Performance Analysis of Maximum Power Point Tracking Algorithms under Varying Irradiation. International Journal of Renewable Energy Development, 6(1), pp. 65–74. doi: 10.14710/ijred.6.1.65-74. Search in Google Scholar

Nassef, A. M., Abdelkareem, M. A., Maghrabie, H. M. and Baroutaji, A. (2023). Review of Metaheuristic Optimization Algorithms for Power Systems Problems. Sustainability, 15(12), p. 9434. doi: 10.3390/su15129434. Search in Google Scholar

Peng, B. R., Ho, K. C. and Liu, Y. H. (2018). A Novel and Fast MPPT Method Suitable for Both Fast Changing and Partially Shaded Conditions. IEEE Transactions on Industrial Electronics, 65(4), pp. 3240–3251. doi: 10.1109/tie.2017.2736484. Search in Google Scholar

Phan, B. C., Lai, Y.-C. and Lin, C. E. (2020). A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition. Sensors, 20(11), p. 3039. doi: 10.3390/s20113039. Search in Google Scholar

Sarwar, S., Hafeez, M. A., Javed, M. Y., Asghar, A. B. and Ejsmont, K. (2020). A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions. Energies, 15(5), p. 1880. doi: 10.3390/en15051880. Search in Google Scholar

Subudhi, B. and Pradhan, R. (2013). A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems. IEEE Transactions on Sustainable Energy, 4(1), pp. 89–98. doi: 10.1109/TSTE.2012.2202294. Search in Google Scholar

Sutikno, T., Samosir, A. S., Aprilianto, R. A., Purnama, H. S., Arsadiando, W. and Padmanaban, S. (2023). Advanced DC–DC Converter Topologies for Solar Energy Harvesting Applications: A Review. Clean Energy, 7, pp. 555–570. doi: 10.1093/ce/zkad003. Search in Google Scholar

Vangari, A., Haribabu, D. and Sakamuri, J. N. (2015). Modeling and control of DC/DC boost converter using K-factor control for MPPT of solar PV system. In: 2015 International Conference on Energy Economics and Environment (ICEEE), Greater Noida, India, September 2015. pp. 1–6. Search in Google Scholar

Villegas-Mier, C. G., Rodriguez-Resendiz, J., Álvarez-Alvarado, J. M., Rodriguez-Resendiz, H., Herrera-Navarro, A. M. and Rodríguez-Abreo, O. (2021). Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review. Micromachines, 12(10), p. 1260. doi: 10.3390/mi12101260. Search in Google Scholar

Wang, Y., Li, Y. and Ruan, X. (2016). High-Accuracy and Fast-Speed MPPT Methods for PV String under Partially Shaded Conditions. IEEE Transactions on Industrial Electronics, 63(1), pp. 235–245. doi: 10.1109/TIE.2015.2465897. Search in Google Scholar

Yadav, D. and Singh, N. (2022). Intelligent techniques for maximum power point tracking. In: Artificial Intelligence for Solar Photovoltaic Systems, 105–127. doi: 10.1201/9781003222286-5. Search in Google Scholar

Yang, Y. and Wen, H. (2018). Adaptive Perturb and Observe Maximum Power Point Tracking with Current Predictive and Decoupled Power Control for Grid-Connected Photovoltaic Inverters. Journal of Modern Power Systems and Clean Energy, 7, pp. 422–432. doi: 10.1007/s40565-018-0437-x. Search in Google Scholar

Yung Yap, K., Sarimuthu, C. R. and Mun-Yee Lim, J. (2020). Artificial Intelligence Based MPPT Techniques for Solar Power System: A review. Journal of Modern Power Systems and Clean Energy, 8(6), pp. 1043–1059. doi: 10.35833/mpce.2020.000159. Search in Google Scholar

Zafar, M. H., Al-shahrani, T., Khan, N. M., Feroz Mirza, A., Mansoor, M., Qadir, M. U., Khan, M. I. and Naqvi, R. A. (2020). Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading. Electronics, 9(11), p. 1962. doi: 10.3390/electronics9111962. Search in Google Scholar

eISSN:
2543-4292
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Computer Sciences, Artificial Intelligence, Engineering, Electrical Engineering, Electronics