SavedAdd your journal

Journals / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 3

Enhanced Efficiency and Robustness in PV Water Pumping Systems via a Novel Weighting Factor-Free MPC with Online Loss Minimization

Badreddine Salhi(University Mohamed Seddik Ben Yahia)0009-0005-1897-4262
Rezzak Daoud(Abbes Laghrour University of Khenchela)0000-0002-1108-1374
Nasserdine Boudjerda(University Mohamed seddik Ben Yahia)0000-0002-3988-6086
Emrah Zerdali(EGE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-1755-0327
Pages
—
Publication Date
2026-07-06
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed MPC is designed for real-time minimization of power losses, thereby maximizing overall system efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Battery-free photovoltaic water pumping systems (PV-WPSs) represent a practical solution for utilizing solar energy in applications such as irrigation and potable water supply, particularly in remote regions.
📊
Key Findings & Data:The first stage employs a hybrid Maximum Power Point Tracking (MPPT) strategy, combining an Artificial Neural Network with Sliding Mode Control (ANN-SM).
💡
Conclusion & Impact:The proposed MPC is designed for real-time minimization of power losses, thereby maximizing overall system efficiency.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Photovoltaic Pumping System · Artificial Neural Network · Sliding Mode Control

Abstract

Battery-free photovoltaic water pumping systems (PV-WPSs) represent a practical solution for utilizing solar energy in applications such as irrigation and potable water supply, particularly in remote regions. This article proposes a standalone PV-WPS featuring a two-stage converter configuration designed to enhance system efficiency and reliability. The first stage employs a hybrid Maximum Power Point Tracking (MPPT) strategy, combining an Artificial Neural Network with Sliding Mode Control (ANN-SM). This technique is selected for its rapid and robust performance under highly dynamic atmospheric conditions, ensuring maximum power extraction from the PV panels. The primary contribution of this work lies in the second stage, where a novel Model Predictive Control (MPC) algorithm is developed for the three-phase inverter driving the induction motor (IM). The proposed MPC is designed for real-time minimization of power losses, thereby maximizing overall system efficiency. A key innovation of this approach is the elimination of weighting factors, which are typically difficult to tune and can limit the implementation and reliability of traditional MPC systems under varying operating conditions. The entire PV-WPS is modeled and simulated in MATLAB/Simulink under fast-changing environmental profiles. A comparative analysis demonstrates that the proposed system significantly improves both tracking performance and overall efficiency. In particular, the ANN-SM MPPT enhances tracking efficiency by approximately 3.92% and reduces power ripple by 95.4% compared to conventional methods. Furthermore, the proposed weighting-factor-free MPC with online loss minimization reduces motor power losses by 16.67% and improves dynamic response, achieving a 43.75% faster settling time. These improvements result in faster response, reduced torque ripples, and increased water pumping capability. Ultimately, by removing the need for weighting factors, the proposed approach simplifies controller design and improves robustness, while the integration of online loss minimization maximizes solar energy utilization, representing a significant advancement in PV-WPS technology.

🔖

Cite This Article

Salhi, B., Daoud, R., Boudjerda, N., Zerdali, E. (2026). Enhanced Efficiency and Robustness in PV Water Pumping Systems via a Novel Weighting Factor-Free MPC with Online Loss Minimization. Turkish Journal of Engineering, Volume: 10 Issue: 3. https://doi.org/10.31127/tuje.1817837

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (31)

  1. Bayhan, B., & Arslan, G. (2018). Applicability of solar and wind energy technologies for a non-residential building. Turkish Journal of Engineering, 2(1), 27–34. doi:10.31127/tuje.341462
  2. Khail, A. A., & Polattaş, Z. (2024). Building integration of solar energy systems in Türkiye and world. Turkish Journal of Engineering, 8(2), 341–352. doi:10.31127/tuje.1394547
  3. Abdul-Razzaq, I. K., Sakr, M. M. F., & Ghazi, Y. (2021). Comparison of PV panels MPPT techniques applied to solar water pumping system. International Journal of Power Electronics and Drive Systems (IJPEDS), 12(3), 1813–1822. doi:10.11591/ijpeds.v12.i3.pp1813-1822
  4. Alhaj Omar, F., Pamuk, N., & Kulaksız, A. A. (2023). A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions. Turkish Journal of Engineering, 7(1), 73–81. doi:10.31127/tuje.1032674
  5. Errouha, M., Derouich, A., Motahhir, S., & Zamzoum, O. (2020). Optimal control of induction motor for photovoltaic water pumping system. Technology and Economics of Smart Grids and Sustainable Energy, 5(1), 1–11. doi:10.1007/s40866-020-0078-9
  6. Josephine, R. L., Reddy, Y. G., & Rachaputi, B. P. (2022). Performance analysis of solar PV array fed induction motor drive for irrigation applications. In Lecture Notes in Electrical Engineering (Vol. 793, pp. 51–65). Berlin, Germany: Springer. doi:10.1007/978-981-16-7393-1_5
  7. Tarusan, S. A. A., Jidin, A., & Jamil, M. L. M. (2022). The optimization of torque ripple reduction by using DTC-multilevel inverter. ISA Transactions, 121, 365–379. doi:10.1016/j.isatra.2021.04.005
  8. Gulbudak, O., Gokdag, M., & Komurcugil, H. (2022). Model predictive control strategy for induction motor drive using Lyapunov stability objective. IEEE Transactions on Industrial Electronics, 69(12), 12119–12128. doi:10.1109/TIE.2021.3139237
  9. Ayala, M., Gandoy, J. D., Rodas, J., Gonzalez, O., Gregor, R., & Rivera, M. (2021). A novel modulated model predictive control applied to six-phase induction motor drives. IEEE Transactions on Industrial Electronics, 68(5), 3672–3682. doi:10.1109/TIE.2020.2984425
  10. Ramasamy, V., Sidharthan, R. K., Kannan, R., & Muralidharan, G. (2019). Optimal tuning of model predictive controller weights using genetic algorithm with interactive decision tree for industrial cement kiln process. Processes, 7(12), 938. doi:10.3390/pr7120938
  11. Nguyen, H. H. (2019). Weighting factor design in model predictive control of power electronic converters: An artificial neural network approach (Master's thesis). Retrieved from Aalborg University Digital Repository.
  12. Mahmoud, A. M., & Ahmed, A. Z. D. (2019). Effective model predictive control approach for a faulty induction motor drive. International Review of Electrical Engineering (IREE), 14(4), 314–327. doi:10.15866/iree.v14i4.16837
  13. Mohammed, S. H., Abdulrazzaq, A. A., & Al-Azzawi, W. K. (2022). Benefits of MPP tracking PV system using perturb and observe technique with boost converter. International Journal of Power Electronics and Drive Systems (IJPEDS), 13(4), 2468–2477. doi:10.11591/ijpeds.v13.i4.pp2468-2477
  14. Saoudi, A., Krim, S., & Mimouni, M. F. (2021). Enhanced intelligent closed loop direct torque and flux control of induction motor for standalone photovoltaic water pumping system. Energies, 14(24), 8245. doi:10.3390/en14248245
  15. Yussif, N., Sabry, O. H., Abdel-Khalik, A. S., Ahmed, S., & Mohammed, A. M. (2021). Enhanced quadratic V/f-based induction motor control of solar water pumping system. Energies, 14(1), 104. doi:10.3390/en14010104
  16. Mustafa, F. M., & Mohammed, A. Q. (2023). Design of a battery charging system fed by thermoelectric generator panels using MPPT techniques. Journal of Electrical Engineering, 74(6), 357–364. doi:10.2478/jee-2023-0043
  17. Errouha, M., Motahhir, S., Combe, Q., & Derouich, A. (2021). Intelligent control of induction motor for photovoltaic water pumping system. SN Applied Sciences, 3(9), 777. doi:10.1007/s42452-021-04757-4
  18. Mokhlis, M., Ferfra, M., Vall, H. A., El Idrissi, R., & Ahmed, C. C. (2020). Comparative study between the different MPPT techniques. In Proceedings of the 5th International Conference on Renewable Energies for Developing Countries (REDEC) (pp. 1–5). doi:10.1109/REDEC49234.2020.9163591
  19. The MathWorks Inc. (2018). MATLAB (Version 9.4.0.813654, R2018a) [Software]. Natick, MA: The MathWorks Inc. Retrieved from https://www.mathworks.com
  20. Gevorkov, L., Domínguez-García, J. L., & Romero, L. T. (2023). Review on solar photovoltaic-powered pumping systems. Energies, 16(1), 94. doi:10.3390/en16010094
  21. Gülbudak, O., & Gökdağ, M. (2022). Performance evaluation of model predictive control method for neutral point clamped inverter. Turkish Journal of Engineering, 6(3), 245–250. doi:10.31127/tuje.962857
  22. Mousavi, M. S., Davari, S. A., Nekoukar, V., Garcia, C., & Rodriguez, J. (2021). A robust torque and flux prediction model by a modified disturbance rejection method for finite-set model-predictive control of induction motor. IEEE Transactions on Power Electronics, 36(8), 9322–9333. doi:10.1109/TPEL.2021.3054425
  23. Keçecioğlu, Ö. F., Gani, A., & Şekkeli, M. (2021). Improved hybrid intelligent controller design for MPPT of stand-alone PV system. Turkish Journal of Engineering, 5(1), 20–28. doi:10.31127/tuje.652229
  24. Mishra, M., Mahajan, P., & Garg, R. (2024). Implementation and comparison of metaheuristically modified ANN MPPT controllers under varying solar irradiance conditions. Electrical Engineering, 106(3), 2631–2647. doi:10.1007/s00202-023-02165-y
  25. Ngo, V. Q. B., Latifi, M., Abbassi, R., Jerbi, H., Ohshima, K., & Khaksar, M. (2021). Improved krill herd algorithm based sliding mode MPPT controller for variable step size P&O method in PV system under simultaneous change of irradiance and temperature. Journal of the Franklin Institute, 358(7), 3491–3511. doi:10.1016/j.jfranklin.2021.02.021
  26. Rajamand, S. (2022). A novel sliding mode control and modified PSO-modified P&O algorithms for peak power control of PV. ISA Transactions, 130, 533–552. doi:10.1016/j.isatra.2022.04.009
  27. Hamdi, T., Elleuch, K., Abid, H., & Toumi, A. (2023). Sliding mode controller with fuzzy supervisor for MPPT of photovoltaic pumping system. International Journal of Power Electronics and Drive Systems (IJPEDS), 14(3), 1639–1650. doi:10.11591/ijpeds.v14.i3.pp1639-1650
  28. Kihal, A., Krim, F., Laib, A., Talbi, B., & Afghoul, H. (2019). An improved MPPT scheme employing adaptive integral derivative sliding mode control for photovoltaic systems under fast irradiation changes. ISA Transactions, 87, 297–306. doi:10.1016/j.isatra.2018.11.020
  29. Saady, I., Karim, M., Bossoufi, B., El Ouanjli, N., Motahhir, S., & Majout, B. (2023). Optimization and control of photovoltaic water pumping system using kalman filter based MPPT and multilevel inverter fed DTC-IM. Results in Engineering, 17, 100829. doi:10.1016/j.rineng.2022.100829
  30. Belgacem, A., Miloud, Y., Mostefai, M., & Belgacem, F. (2022). Fuzzy logic direct torque control of induction motor for photovoltaic water pumping system. International Journal of Power Electronics and Drive Systems (IJPEDS), 13(3), 1822–1832. doi:10.11591/ijpeds.v13.i3.pp1822-1832
  31. Massaq, Z., Chbirik, G., Abounada, A., Brahmi, A., & Ramzi, M. (2020). Control of photovoltaic water pumping system employing non-linear predictive control and fuzzy logic control. International Review on Modeling and Simulations (IREMOS), 13(6), 373–382. doi:10.15866/iremos.v13i6.18615