An Efficient Grey Wolf Optimization Approach for Maximum Power Point Tracking of Solar Photovoltaic Systems
DOI:
https://doi.org/10.15662/IJEETR.2026.0802143Keywords:
Maximum Power Point Tracking (MPPT), Grey Wolf Optimization (GWO), Metaheuristic Algorithm, DC–DC Boost Converter, Renewable Energy, Solar Irradiance, Duty Cycle ControlAbstract
Solar photovoltaic (PV) systems are widely regarded as one of the most sustainable and environmentally friendly energy sources for power generation. In recent years, their adoption has significantly increased due to the growing demand for clean energy in electrical applications. However, the output power of a PV system is highly dependent on environmental conditions such as solar irradiance and temperature, which leads to fluctuations in its performance. To ensure maximum efficiency, it is essential to operate the PV system at its Maximum Power Point (MPP).Maximum Power Point Tracking (MPPT) techniques are therefore crucial for optimizing the energy extraction from PV systems. Conventional methods such as the Perturb and Observe (P&O) algorithm suffer from inherent limitations, particularly steady-state oscillations around the MPP, which result in power losses and reduced efficiency.To overcome these drawbacks, this work introduces a Grey Wolf Optimization (GWO)-based MPPT approach. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves, and it is employed here to effectively track the MPP under varying environmental conditions. In this method, the PV system’s voltage and current are taken as input parameters, while the duty cycle of the converter serves as the control output
.A DC-DC boost converter is integrated into the system to regulate and enhance the output voltage according to variations in input power. The proposed method is evaluated under different operating conditions and compared with the conventional P&O technique. Simulation results demonstrate that the GWO-based MPPT method achieves improved power output and exhibits faster tracking capability, especially under changing irradiance levels
References
1. PremkumarManoharan , Umashankar Subramaniam , Thanikanti Sudhakar Babu, Sanjeevi kumar Padmanaban - Improved Perturb and Observation Maximum Power Point Tracking Technique for Solar Photovoltaic Power Generation Systems 2020 doi: 10.1109/JSYST.2020.3003255.
2. Mohamad Amin Ghasemi, Hossein Mohammadian Foroushani, Frede Blaabjerg - Marginal Power-Based Maximum Power Point Tracking Control of Photovoltaic System under Partially Shaded Condition IEEE Transactions on Power Electronics 2020 doi: 10.1109/TPEL.2019.2952972
3. Dhaouadi GUIZA, Djamel OUNNAS- Implementation of Modified Perturb and Observe Based MPPT Algorithm for Photovoltaic System Transactions on Power Electronics, 2019 doi:10.1109/JSYST.2019.9182483
4. 4.Oluwaseun M. Akeyo ,Vandana Rallabandi, NicholasJewell, Dan M. Ionel The Design and Analysis of Large Solar PV Farm Configurations With DC Connected Battery Systems 2019doi: 10.1109/TIA.2020.2969102
5. Mohammed A. Elgendy, Bashar Zahawi, David J. Atkinson, “Assessment of the Incremental Conductance Maximum Power Point Tracking Algorithm” IEEE Transactions on Sustainable Energy, Vol.4, No.1, Jan 2013.
6. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
7. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
8. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
10. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
11. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
12. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
13. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
14. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
15. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
16. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
17. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
18. 6. B. Subudhi and R. Pradhan, ‘‘A comparative study on maximum powerpoint tracking techniques for photovoltaic power systems,’’ IEEE Trans. Sustain. Energy, vol.4, no.1, pp.89–98, Jan 2013.
19. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., & Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
20. Sugumar, R. (2026). Performance Optimization Frameworks for Financial Web Platforms with Real-Time Transaction Processing. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 600-611.
21. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.
22. Udayakumar, R., Yogesh Pansambal, S., Anbazhagan, K., & Sugumar, R. Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migr Lett. 2023; 20 (4): 33–42.
23. Vimal, V. R. (2025). Hybrid Nature-Inspired Optimization and Machine Learning Techniques for Cardiac Disease Detection. SGS-Engineering & Sciences, 1(3).
24. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
25. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
26. Mathew, A. (2025). Human–AI Collaboration in Security Operations: Measuring Alert Trust, Automation Bias, and Analyst Upskilling in AI-Augmented SOC Environments. International Journal of Computer Technology and Electronics Communication, 8(5), 11375-11380.
27. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
28. Prasad, D. R., & Vimal, V. R. (2025, April). Early Detection of Brain Tumor from The MRI scan Using Deep Learning. In 2025 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) (pp. 1-5). IEEE.
29. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
30. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.
31. Mathew, A. (2025). Human–AI Collaboration in Security Operations: Measuring Alert Trust, Automation Bias, and Analyst Upskilling in AI-Augmented SOC Environments. International Journal of Computer Technology and Electronics Communication, 8(5), 11375-11380.
32. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
33. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
34. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
35. Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
