volume | PIER Journals

Abstract

This paper introduces an intelligent Maximum Power Point Tracking (MPPT) framework for photovoltaic systems that achieves significant performance gains through two primary innovations: a dynamic search space optimization that intelligently constrains the search region to approximately 2% of the conventional area, and a sophisticated Q-learning algorithm operating within this optimized region. The framework establishes a real-time relationship between environmental conditions and maximum power point parameters for this aggressive search space reduction. For complex partial shading conditions, an adaptive switching mechanism dynamically activates an enhanced meta-heuristic optimization component with improved convergence properties, ensuring appropriate algorithm selection based on detected operating conditions. Experimental results demonstrate that under uniform irradiance, the framework achieves 99.12% tracking efficiency (a 3.34% improvement over P&O). Under rapidly changing conditions, it maintains 97.83% efficiency (compared to P&O's 90.12%), and under partial shading, it achieves 95.89% global MPPT efficiency (versus 76.25% for P&O). The proposed method significantly reduces steady-state oscillations to 0.41% (from 1.87% for P&O) and offers 42.3% faster convergence. While requiring moderately higher computational resources, the approach is implementable on medium-range microcontrollers, balancing performance with practical deployment.

1. Owusu, Phebe Asantewaa and Samuel Asumadu-Sarkodie, "A review of renewable energy sources, sustainability issues and climate change mitigation," Cogent Engineering, Vol. 3, No. 1, 1167990, 2016. Google Scholar

2. Olabi, A. G. and Mohammad Ali Abdelkareem, "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Vol. 158, 112111, 2022. Google Scholar

3. Esram, Trishan and Patrick L. Chapman, "Comparison of photovoltaic array maximum power point tracking techniques," IEEE Transactions on Energy Conversion, Vol. 22, No. 2, 439-449, 2007. Google Scholar

4. Tchouani Njomo, Arnaud Flanclair, Godpromesse Kenne, Rostand Marc Douanla, and Lionel Leroy Sonfack, "A modified ESC algorithm for MPPT applied to a photovoltaic system under varying environmental conditions," International Journal of Photoenergy, Vol. 2020, No. 1, 1956410, 2020. Google Scholar

5. Eltawil, Mohamed A. and Zhengming Zhao, "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Vol. 25, 793-813, 2013. Google Scholar

6. Alik, Rozana and Awang Jusoh, "Modified Perturb and Observe (P&O) with checking algorithm under various solar irradiation," Solar Energy, Vol. 148, 128-139, 2017. Google Scholar

7. Kumar, Dilip, Yogesh Kumar Chauhan, Ajay Shekhar Pandey, Ankit Kumar Srivastava, Varun Kumar, Faisal Alsaif, Rajvikram Madurai Elavarasan, Md. Rabiul Islam, Raju Kannadasan, and Mohammed H. Alsharif, "A novel hybrid MPPT approach for solar PV systems using particle-swarm-optimization-trained machine learning and flying squirrel search optimization," Sustainability, Vol. 15, No. 6, 5575, 2023. Google Scholar

8. Tao, Hai, Mehrdad Ghahremani, Faraedoon Waly Ahmed, Wang Jing, Muhammad Shahzad Nazir, and Kentaro Ohshima, "A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions," Control Engineering Practice, Vol. 112, 104809, 2021. Google Scholar

9. He, Yanxiao, Qing Yang, Lingyu Huang, Mandan Luo, Hongwen Liu, and Jisheng Huang, "Frequency optimization of PZT-FBG voltage sensor based on temperature-independent demodulation method," IEEE Sensors Journal, Vol. 21, No. 23, 26821-26829, 2021. Google Scholar

10. Bhatnagar, Pallavee and R. K. Nema, "Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Vol. 23, 224-241, 2013. Google Scholar

11. Reisi, Ali Reza, Mohammad Hassan Moradi, and Shahriar Jamasb, "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Vol. 19, 433-443, 2013. Google Scholar

12. Yilmaz, Unal, Ali Kircay, and Selim Borekci, "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Vol. 81, 994-1001, 2018. Google Scholar

13. Radjai, Tawfik, Jean Paul Gaubert, Lazhar Rahmani, and Saad Mekhilef, "Experimental verification of P&O MPPT algorithm with direct control based on Fuzzy logic control using CUK converter," International Transactions on Electrical Energy Systems, Vol. 25, No. 12, 3492-3508, 2015. Google Scholar

14. Harrison, Ambe, Cyrille Feudjio, Christophe Raoul Fotso Mbobda, and Njimboh Henry Alombah, "A new framework for improving MPPT algorithms through search space reduction," Results in Engineering, Vol. 22, 101998, 2024. Google Scholar

15. Yilmaz, Unal, Omer Turksoy, and Ahmet Teke, "Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions," International Journal of Electrical Power & Energy Systems, Vol. 113, 634-651, 2019. Google Scholar

16. Kamran, Muhammad, Muhammad Mudassar, Muhammad Rayyan Fazal, Muhammad Usman Asghar, Muhammad Bilal, and Rohail Asghar, "Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system," Journal of King Saud University --- Engineering Sciences, Vol. 32, No. 7, 432-441, 2020. Google Scholar

17. Chao, Kuei-Hsiang and Muhammad Nursyam Rizal, "A hybrid MPPT controller based on the genetic algorithm and ant colony optimization for photovoltaic systems under partially shaded conditions," Energies, Vol. 14, No. 10, 2902, 2021. Google Scholar

18. Femia, N., G. Petrone, G. Spagnuolo, and M. Vitelli, "Optimization of perturb and observe maximum power point tracking method," IEEE Transactions on Power Electronics, Vol. 20, No. 4, 963-973, 2005. Google Scholar

19. Ni, Jiahua and Ji Xiang, "A concise control method based on spatial-domain dp/dv calculation for MPPT/power reserved of PV systems," IEEE Transactions on Energy Conversion, Vol. 38, No. 1, 3-14, 2023. Google Scholar

20. Li, Xingshuo, Huiqing Wen, and Chenhao Zhao, "Improved beta parameter based MPPT method in photovoltaic system," 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), 1405-1412, Seoul, Korea (South), 2015.

21. Titri, Sabrina, Cherif Larbes, Kamal Youcef Toumi, and Karima Benatchba, "A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions," Applied Soft Computing, Vol. 58, 465-479, 2017. Google Scholar

22. Mo, Shixun, Qintao Ye, Kunping Jiang, Xiaofeng Mo, and Gengyu Shen, "An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm," Energy Reports, Vol. 8, No. 5, 141-150, 2022. Google Scholar

23. Harrison, Ambe, Njimboh Henry Alombah, and Jean de Dieu Nguimfack Ndongmo, "A new hybrid MPPT based on incremental conductance‐integral backstepping controller applied to a PV system under fast‐changing operating conditions," International Journal of Photoenergy, Vol. 2023, No. 1, 9931481, 2023. Google Scholar

24. Al-Majidi, Sadeq D., Maysam F. Abbod, and Hamed S. Al-Raweshidy, "A particle swarm optimisation-trained feedforward neural network for predicting the maximum power point of a photovoltaic array," Engineering Applications of Artificial Intelligence, Vol. 92, 103688, 2020. Google Scholar

25. Harrison, Ambe, Njimboh Henry Alombah, and Jean de Dieu Nguimfack Ndongmo, "Solar irradiance estimation and optimum power region localization in PV energy systems under partial shaded condition," Heliyon, Vol. 9, No. 8, e18434, 2023. Google Scholar

26. Ahmed, Jubaer and Zainal Salam, "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Vol. 150, 97-108, 2015. Google Scholar

27. Chellakhi, Abdelkhalek, Said El Beid, and Younes Abouelmahjoub, "Implementation of a novel MPPT tactic for PV system applications on MATLAB/simulink and proteus-based arduino board environments," International Journal of Photoenergy, Vol. 2021, No. 1, 6657627, 2021. Google Scholar

28. Jiang, Lian Lian, Douglas L. Maskell, and Jagdish C. Patra, "A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions," Energy and Buildings, Vol. 58, 227-236, 2013. Google Scholar

29. Zhang, Xibeng, Don Gamage, Benfei Wang, and Abhisek Ukil, "Hybrid maximum power point tracking method based on iterative learning control and perturb & observe method," IEEE Transactions on Sustainable Energy, Vol. 12, No. 1, 659-670, 2021. Google Scholar

30. Zhang, Fan, Kary Thanapalan, Andrew Procter, Stephen Carr, and Jon Maddy, "Adaptive hybrid maximum power point tracking method for a photovoltaic system," IEEE Transactions on Energy Conversion, Vol. 28, No. 2, 353-360, 2013. Google Scholar

31. Rezk, Hegazy, Mokhtar Aly, Mujahed Al-Dhaifallah, and Masahito Shoyama, "Design and hardware implementation of new adaptive fuzzy logic-based MPPT control method for photovoltaic applications," IEEE Access, Vol. 7, 106427-106438, 2019. Google Scholar

32. Li, Hong, Duo Yang, Wenzhe Su, Jinhu Lü, and Xinghuo Yu, "An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading," IEEE Transactions on Industrial Electronics, Vol. 66, No. 1, 265-275, 2019. Google Scholar

33. Mohanty, Satyajit, Bidyadhar Subudhi, and Pravat Kumar Ray, "A grey wolf-assisted perturb & observe MPPT algorithm for a PV system," IEEE Transactions on Energy Conversion, Vol. 32, No. 1, 340-347, 2017. Google Scholar

34. Gupta, Ankit, Yogesh K. Chauhan, and Rupendra Kumar Pachauri, "A comparative investigation of maximum power point tracking methods for solar PV system," Solar Energy, Vol. 136, 236-253, 2016. Google Scholar

35. Kermadi, Mostefa, Zainal Salam, Jubaer Ahmed, and El Madjid Berkouk, "An effective hybrid maximum power point tracker of photovoltaic arrays for complex partial shading conditions," IEEE Transactions on Industrial Electronics, Vol. 66, No. 9, 6990-7000, 2019. Google Scholar

36. Swetha, K. T., Barry Venugopal Reddy, and Rohit Kumar Jain, "A direct search nelder mead MPPT based induction motor drive for solar PV water pumping system," IEEE Transactions on Industry Applications, Vol. 61, No. 1, 607-617, 2025. Google Scholar

37. Eto, Yasushi, Yuichi Noge, Masahito Shoyama, and Tadatoshi Babasaki, "Control system of PV source for DC distribution system with seamless operation transition between I-V droop control and current control for MPPT," IEEE Transactions on Industry Applications, Vol. 60, No. 2, 3638-3652, 2024. Google Scholar

38. Basheer, Ameerkhan Abdul, Jae Hoon Jeong, Seong Ryong Lee, and Young Hoon Joo, "Power maximization using finite-control-set model predictive control strategy for wind turbine systems," IEEE Journal of Emerging and Selected Topics in Industrial Electronics, Vol. 6, No. 1, 238-247, 2025. Google Scholar

39. Meineri, Nicolás Agüero, Ignacio Santana, and Ignacio Galiano Zurbriggen, "Ultra-fast MPPT for residential PV systems with low DC-link capacitance and differential power processing," IEEE Transactions on Power Electronics, Vol. 40, No. 2, 2736-2745, 2025. Google Scholar

40. Kumari, Pushpa, Nishant Kumar, and Bijaya Ketan Panigrahi, "Rayleigh distribution-based novel and efficient MPPT algorithm for rooftop PV system with competence to distinguish different dynamics," IEEE Transactions on Consumer Electronics, Vol. 70, No. 1, 58-67, 2024. Google Scholar

41. Qi, Zhiyuan and Ruyi Zhang, "Coordinated scheduling of micro-grid combined heat and power based on dynamic feedback correction and virtual penalty cost," IEEE Transactions on Power Systems, Vol. 39, No. 3, 4975-4986, 2024. Google Scholar

42. Rakhshan, Mohsen, Navid Vafamand, Mohammad-Hassan Khooban, and Frede Blaabjerg, "Maximum power point tracking control of photovoltaic systems: A polynomial fuzzy model-based approach," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 6, No. 1, 292-299, 2018. Google Scholar

43. Rahul, Immadisetty and Raju Hariharan, "Enhancement of solar PV panel efficiency using double integral sliding mode MPPT control," Tsinghua Science and Technology, Vol. 29, No. 1, 271-283, 2024. Google Scholar

Ms. Xiaoping Lei