Intelligent power control strategy based on self-tuning fuzzy MPPT for grid-connected hybrid system

Authors

DOI:

https://doi.org/10.20998/2074-272X.2025.3.04

Keywords:

maximum power point tracking algorithm, hybrid renewable energy system, conventional controller, fuzzy logic controller, self-tuning fuzzy logic control

Abstract

Introduction. This paper investigates various methods for controlling the Maximum Power Point Tracking (MPPT) algorithm within the framework of intelligent energy control for grid-connected Hybrid Renewable Energy Systems (HRESs). The purpose of the study is to improve the efficiency and reliability of the power supply in the face of unpredictable weather conditions and diverse energy sources. Intelligent control techniques are used to optimize the extraction of energy from available sources and effectively regulate energy distribution throughout the system. Novelty study is employing intelligent control strategies for both energy optimization and control. This research distinguishes itself from conventional approaches, particularly through the application of Self-Tuning Fuzzy Logic Control (ST-FLC) and fuzzy tracking. Unlike conventional methods that rely on logical switches, this intelligent strategy utilizes fuzzy rules adapted to different operating modes for more sophisticated energy control. The proposed control strategy minimizes static errors and ripples in the direct current bus and challenges in meeting load demands. Methods of this research includes a comprehensive analysis of several optimization techniques under varying weather scenarios. The proposed strategy generates three control signals that correspond to selected energy sources based on solar irradiation, wind velocity and battery charging status. Practical value. ST-FLC technique outperforms both conventional methods and standard Fuzzy Logic Controllers (FLCs). It consistently delivers superior performance during set point and load disturbance phases. The simulation, conducted using MATLAB/Simulink. The results indicate that fuzzy proposed solution enables the system to adapt effectively to various operational scenarios, displaying the practical applicability of the proposed strategies. This study presents a thorough evaluation of intelligent control methods for MPPT in HRESs, emphasizing their potential to optimize energy supply under varying conditions. References 27, tables 6, figures 18.

Author Biographies

H. Chaib, University of Tiaret

PhD Student, Laboratory of Energy Engineering and Computer Engineering

S. Hassaine, University of Tiaret

PhD, Professor, Laboratory of Energy Engineering and Computer Engineering

Y. Mihoub, University of Tiaret

PhD, Associate Professor, Laboratory of Energy Engineering and Computer Engineering

S. Moreau, Poitiers University

PhD, Associate Professor, Laboratory of Informatics and Automatic Systems (LIAS)

References

Renewables 2024. Global Status Report. Global Overview 2024. Available at: https://www.ren21.net/gsr2024_GO_report (Accessed: 02 July 2024).

Muthukaruppasamy S., Dharmaprakash R., Sendilkumar S., Parimalasundar E. Enhancing off-grid wind energy systems with controlled inverter integration for improved power quality. Electrical Engineering & Electromechanics, 2024, no. 5, pp. 41-47. doi: https://doi.org/10.20998/2074-272X.2024.5.06.

Shivam K., Tzou J.-C., Wu S.-C. A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique. Energy Conversion and Management, 2021, vol. 237, art. no. 114103. doi: https://doi.org/10.1016/j.enconman.2021.114103.

Lamzouri F.E.Z., Boufounas E.-M., Amrani A.El. Efficient energy management and robust power control of a stand-alone wind-photovoltaic hybrid system with battery storage. Journal of Energy Storage, 2021, vol. 42, art. no. 103044. doi: https://doi.org/10.1016/j.est.2021.103044.

Benlahbib B., Bouarroudj N., Mekhilef S., Abdeldjalil D., Abdelkrim T., Bouchafaa F., Lakhdari A. Experimental investigation of power management and control of a PV/wind/fuel cell/battery hybrid energy system microgrid. International Journal of Hydrogen Energy, 2020, vol. 45, no. 53, pp. 29110-29122. doi: https://doi.org/10.1016/j.ijhydene.2020.07.251.

Laxman B., Annamraju A., Srikanth N.V. A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. International Journal of Hydrogen Energy, 2021, vol. 46, no. 18, pp. 10653-10665. doi: https://doi.org/10.1016/j.ijhydene.2020.12.158.

Suresh G., Prasad D., Gopila M. An efficient approach based power flow management in smart grid system with hybrid renewable energy sources. Renewable Energy Focus, 2021, vol. 39, pp. 110-122. doi: https://doi.org/10.1016/j.ref.2021.07.009.

Bounechba H., Boussaid A., Bouzid A. Experimental validation of fuzzy logic controller based on voltage perturbation algorithm in battery storage photovoltaic system. Electrical Engineering & Electromechanics, 2024, no. 5, pp. 20-27. doi: https://doi.org/10.20998/2074-272X.2024.5.03.

Zerzouri N., Ben Si Ali N., Benalia N. A maximum power point tracking of a photovoltaic system connected to a three-phase grid using a variable step size perturb and observe algorithm. Electrical Engineering & Electromechanics, 2023, no. 5, pp. 37-46. doi: https://doi.org/10.20998/2074-272X.2023.5.06.

Jately V., Azzopardi B., Joshi J., Venkateswaran V.B., Sharma A., Arora S. Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels. Renewable and Sustainable Energy Reviews, 2021, vol. 150, art. no. 111467. doi: https://doi.org/10.1016/j.rser.2021.111467.

Louarem S., Kebbab F.Z., Salhi H., Nouri H. A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system. Electrical Engineering & Electromechanics, 2022, no. 4, pp. 27-33. doi: https://doi.org/10.20998/2074-272X.2022.4.04.

Kaddache M., Drid S., Khemis A., Rahem D., Chrifi-Alaoui L. Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator. Electrical Engineering & Electromechanics, 2024, no. 2, pp. 61-66. doi: https://doi.org/10.20998/2074-272X.2024.2.09.

Mahgoun M.S., Badoud A.E. New design and comparative study via two techniques for wind energy conversion system. Electrical Engineering & Electromechanics, 2021, no. 3, pp. 18-24. doi: https://doi.org/10.20998/2074-272X.2021.3.03.

Farah N., Talib M.H.N., Mohd Shah N.S., Abdullah Q., Ibrahim Z., Lazi J.B.M., Jidin A. A Novel Self-Tuning Fuzzy Logic Controller Based Induction Motor Drive System: An Experimental Approach. IEEE Access, 2019, no. 7, pp. 68172-68184. doi: https://doi.org/10.1109/ACCESS.2019.2916087.

Marugán A.P., Márquez F.P.G., Perez J.M.P., Ruiz-Hernández D. A survey of artificial neural network in wind energy systems. Applied Energy, 2018, vol. 228, pp. 1822-1836. doi: https://doi.org/10.1016/j.apenergy.2018.07.084.

Mansouri M., Bey M., Hassaine S., Larbi M., Allaoui T., Denai M. Genetic algorithm optimized robust nonlinear observer for a wind turbine system based on permanent magnet synchronous generator. ISA Transactions, 2022, vol. 129, pp. 230-242. doi: https://doi.org/10.1016/j.isatra.2022.02.004.

Guentri H., Allaoui T., Mekki M., Denai M. Power management and control of a photovoltaic system with hybrid battery-supercapacitor energy storage based on heuristics methods. Journal of Energy Storage, 2021, vol. 39, art. no. 102578. doi: https://doi.org/10.1016/j.est.2021.102578.

Sabhi K., Talea M., Bahri H., Dani S. Integrating dual active bridge DC-DC converters: a novel energy management approach for hybrid renewable energy systems. Electrical Engineering & Electromechanics, 2025, no. 2, pp. 39-47. doi: https://doi.org/10.20998/2074-272X.2025.2.06.

Ayat Y., Badoud A.E., Mekhilef S., Gassab S. Energy management based on a fuzzy controller of a photovoltaic/fuel cell/Li-ion battery/supercapacitor for unpredictable, fluctuating, high-dynamic three-phase AC load. Electrical Engineering & Electromechanics, 2023, no. 3, pp. 66-75. doi: https://doi.org/10.20998/2074-272X.2023.3.10.

Sharma R.K., Mudaliyar S., Mishra S. A DC Droop-Based Optimal Dispatch Control and Power Management of Hybrid Photovoltaic-Battery and Diesel Generator Standalone AC/DC System. IEEE Systems Journal, 2021, vol. 15, no. 2, pp. 3012-3023. doi: https://doi.org/10.1109/JSYST.2020.3032887.

Kamel A.A., Rezk H., Abdelkareem M.A. Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy. International Journal of Hydrogen Energy, 2021, vol. 46, no. 8, pp. 6061-6075. doi: https://doi.org/10.1016/j.ijhydene.2020.06.052.

Ali Moussa M., Derrouazin A., Latroch M., Aillerie M. A hybrid renewable energy production system using a smart controller based on fuzzy logic. Electrical Engineering & Electromechanics, 2022, no. 3, pp. 46-50. doi: https://doi.org/10.20998/2074-272X.2022.3.07.

Toghani Holari Y., Taher S.A., Mehrasa M. Power management using robust control strategy in hybrid microgrid for both grid-connected and islanding modes. Journal of Energy Storage, 2021, vol. 39, art. no. 102600. doi: https://doi.org/10.1016/j.est.2021.102600.

Charrouf O., Betka A., Abdeddaim S., Ghamri A. Artificial Neural Network power manager for hybrid PV-wind desalination system. Mathematics and Computers in Simulation, 2020, vol. 167, pp. 443-460. doi: https://doi.org/10.1016/j.matcom.2019.09.005.

Taghdisi M., Balochian S. Maximum Power Point Tracking of Variable-Speed Wind Turbines Using Self-Tuning Fuzzy PID. Technology and Economics of Smart Grids and Sustainable Energy, 2020, vol. 5, no. 1, art. no. 13. doi: https://doi.org/10.1007/s40866-020-00087-3.

Roumila Z., Rekioua D., Rekioua T. Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery. International Journal of Hydrogen Energy, 2017, vol. 42, no. 30, pp. 19525-19535. doi: https://doi.org/10.1016/j.ijhydene.2017.06.006.

Singh P., Lather J.S. Power management and control of a grid-independent DC microgrid with hybrid energy storage system. Sustainable Energy Technologies and Assessments, 2021, vol. 43, art. no. 100924. doi: https://doi.org/10.1016/j.seta.2020.100924.

Downloads

Published

2025-05-02

How to Cite

Chaib, H., Hassaine, S., Mihoub, Y., & Moreau, S. (2025). Intelligent power control strategy based on self-tuning fuzzy MPPT for grid-connected hybrid system. Electrical Engineering & Electromechanics, (3), 23–30. https://doi.org/10.20998/2074-272X.2025.3.04

Issue

No. 3 (2025)

Section

Electrotechnical complexes and Systems

License

Copyright (c) 2025 H. Chaib, S. Hassaine, Y. Mihoub, S. Moreau

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.