This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA's reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system's response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

College of Computer Science and Information Technology Al Razi University Sana'a 1152 Yemen

College of Electrical and Information Engineering Hunan University Hunan 410083 China

College of Engineering University of Business and Technology Jeddah 21448 Saudi Arabia

Department of Electrical and Electronics Faculty of Engineering Alberoni University Kapisa Afghanistan

Department of Electrical Engineering Graphic Era Dehradun 248002 India

Department of Electrical Engineering Jubail Industrial College Jubail Saudi Arabia

Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria 21544 Egypt

Engineering and Sustainable Development Research Team EST of Dakhla Ibn Zohr University Dakhla Morocco

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

Laboratory of Applied Automatic Department of automation and electrification of industrial process Faculty of Hydrocarbons and Chemistry M'hamed Bougara University of Boumerdes Boumerdes Algeria

See more in PubMed

Satpathy, P. R., Aljafari, B., Thanikanti, S. B., Nwulu, N. & Sharma, R. A multi-string differential power processing based voltage equalizer for partial shading detection and mitigation in PV arrays. Alexandria Eng. J.104, 12–30 (2024).

Fathy, A., Amer, D. A. & Al-Dhaifallah, M. Modified tunicate swarm algorithm-based methodology for enhancing the operation of partially shaded photovoltaic system. Alexandria Eng. J.79, 449–470 (2023).

AL-Wesabi, I. et al. Hybrid SSA-PSO based intelligent direct sliding-mode control for extracting maximum photovoltaic output power and regulating the DC-bus voltage. Int. J. Hydrogen Energy. 10.1016/j.ijhydene.2023.10.034 (2023).

Ahmed, M., Harbi, I., Kennel, R., Rodriguez, J. & Abdelrahem, M. An improved photovoltaic maximum power point tracking technique-based model predictive control for fast atmospheric conditions. Alexandria Eng. J.63, 613–624 (2023).

EL-Banna, M. H. et al. On-grid optimal MPPT for fine-tuned inverter based PV system using golf optimizer considering partial shading effect. Alexandria Eng. J.103, 180–196 (2024).

Yagoub, M. A., Elserougi, A. A. & Abdelhamid, T. H. Closed-loop string current control for string optimizer circuits-based PV system to enhance extracted PV power during partial shading conditions. Alexandria Eng. J.61, 11159–11170 (2022).

Ibrahim, A. W. et al. Optimized Energy Management Strategy for an Autonomous DC Microgrid integrating PV/Wind/Battery/Diesel-Based hybrid PSO-GA-LADRC through SAPF. Technologies12. 10.3390/technologies12110226 (2024).

Altwallbah, N. M. M., Radzi, M. A. M., Azis, N., Shafie, S. & Zainuri, M. A. A. M. New perturb and observe algorithm based on trapezoidal rule: uniform and partial shading conditions. Energy Convers. Manag.264, 115738 (2022).

Al-Wesabi, I., Fang, Z., Hussein Farh, H. M., Al-Shamma’a, A. A. & Al-Shaalan, A. M. Comprehensive comparisons of improved incremental conductance with the state-of-the-art MPPT techniques for extracting global peak and regulating dc-link voltage. Energy Rep.11, 1590–1610. 10.1016/j.egyr.2024.01.020 (2024).

Aly, M. & Rezk, H. An improved fuzzy logic control-based MPPT method to enhance the performance of PEM fuel cell system. Neural Comput. Appl.34, 4555–4566. 10.1007/s00521-021-06611-5 (2022).

Sibtain, D. et al. Stability analysis and design of variable step-size P&O algorithm based on fuzzy robust tracking of MPPT for standalone/grid connected power system. Sustainability14, 8986 (2022).

Nasir, A., Rasool, I., Sibtain, D. & Kamran, R. Adaptive fractional order PID controller based MPPT for PV connected grid system under changing weather conditions. J. Electr. Eng. Technol.16, 2599–2610 (2021).

Usman Khan, F., Gulzar, M. M., Sibtain, D., Usman, H. M. & Hayat, A. Variable step size fractional incremental conductance for MPPT under changing atmospheric conditions. Int. J. Numer. Model. Electron. Networks Devices Fields33, e2765 (2020).

Sibtain, D., Murtaza, A. F., Ahmed, N., Sher, H. A. & Gulzar, M. M. Multi control adaptive fractional order PID control approach for PV/wind connected grid system. Int. Trans. Electr. Energy Syst.31, e12809 (2021).

Rekioua, D. et al. Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach. Sci. Rep.14, 30013. 10.1038/s41598-024-78847-9 (2024). PubMed PMC

Abdelmalek, F. et al. Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems. Sci. Rep.14, 26047. 10.1038/s41598-024-77488-2 (2024). PubMed PMC

Bouguerra, A. et al. Enhancing PEM fuel cell efficiency with flying squirrel search optimization and cuckoo search MPPT techniques in dynamically operating environments. Sci. Rep.14, 13946. 10.1038/s41598-024-64915-7 (2024). PubMed PMC

Rekioua, D. et al. Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms. Sci. Rep.14, 10267. 10.1038/s41598-024-60116-4 (2024). PubMed PMC

Zaghba, L. et al. Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions. Sci. Rep.14, 8205. 10.1038/s41598-024-59024-4 (2024). PubMed PMC

Deghfel, N. et al. A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems. Sci. Rep.14, 6827. 10.1038/s41598-024-57610-0 (2024). PubMed PMC

Hamed, S. et al. A robust MPPT approach based on first-order sliding mode for triple-junction photovoltaic power system supplying electric vehicle. Energy Rep.9, 4275–4297 (2023).

Belmadani, H. et al. Guided Seagull Optimization for Improved PV MPPT in Partial Shading,., IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), Bhubaneswar, India, 2023, pp. 1–5. 10.1109/AESPC59761.2023.10390053 (2023).

Kalaiarasi, N., Sivapriya, A. & Vishnuram Pradeep, Pushkarna, Mukesh, Bajaj, Mohit, Kotb, Hossam, Alphonse, Sadam, Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques, International Transactions on Electrical Energy Systems. 1134633, 16. 10.1155/2023/1134633 (2023).

Aghaloo, K., Ali, T., Chiu, Y. R. & Sharifi, A. Optimal site selection for the solar-wind hybrid renewable energy systems in Bangladesh using an integrated GIS-based BWM-fuzzy logic method. Energy Convers. Manag.283, 116899 (2023).

Lasheen, M. & Abdel-Salam, M. Maximum power point tracking using Hill climbing and ANFIS techniques for PV applications: a review and a novel hybrid approach. Energy Convers. Manag.171, 1002–1019 (2018).

Ibrahim, A. et al. Artificial neural network based maximum power point tracking for PV system, in: 2019 Chinese Control Conf., IEEE, pp. 6559–6564. (2019).

Ibrahim, A. et al. PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions. Chin. J. Electr. Eng.6, 106–121 (2020).

Titri, S., Larbes, C., Toumi, K. Y. & Benatchba, K. A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Appl. Soft Comput.58, 465–479 (2017).

Tyagi, S., Singh, P. K. & Tiwari, A. K. Advancements in performance of zinc oxide/carbon quantum dots based photovoltaic trigeneration system using genetic algorithm and particle swarm optimization. Sustain. Energy Technol. Assess.60, 103501 (2023).

Al-Wesabi, I. et al. Cuckoo search combined with PID controller for maximum power extraction of partially shaded photovoltaic system. Energies15, 2513 (2022).

González-Castaño, C., Restrepo, C., Kouro, S. & Rodriguez, J. MPPT algorithm based on artificial bee colony for PV system. IEEE Access.9, 43121–43133 (2021).

Da Rocha, M. V., Sampaio, L. P. & da Silva, S. A. O. Comparative analysis of MPPT algorithms based on Bat algorithm for PV systems under partial shading condition. Sustain. Energy Technol. Assessments. 40, 100761 (2020).

Yang, B. et al. Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions. Energy Convers. Manag.292, 117410 (2023).

Hemalatha, S., Banu, G. & Indirajith, K. Design and investigation of PV string/central architecture for bayesian fusion technique using grey wolf optimization and flower pollination optimized algorithm. Energy Convers. Manag.286, 117078 (2023).

MiarNaeimi, F., Azizyan, G. & Rashki, M. Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Syst.213, 106711 (2021).

Mansoor, M., Mirza, A. F. & Ling, Q. Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. J. Clean. Prod.274, 122857 (2020).

Refaat, A. et al. Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions. Renew. Energy220, 119718. 10.1016/j.renene.2023.119718 (2024).

Sarwar, S., Hafeez, M. A., Javed, M. Y., Asghar, A. B. & Ejsmont, K. A horse herd optimization algorithm (HOA)-Based MPPT technique under partial and complex partial shading conditions. Energies15, 0–23. 10.3390/en15051880 (2022).

Abbassi, R. Design of a Novel chaotic horse herd optimizer and application to MPPT for Optimal performance of stand-alone solar PV Water Pumping systems, 1–26. (2024).

Gouvêa, J. A., Fernandes, L. M., Pinto, M. F. & Zachi, A. R. L. Variant ADRC design paradigm for controlling uncertain dynamical systems. Eur. J. Control72, 100822 (2023).

Ben Messaoud, S., Belkhiri, M., Belkhiri, A. & Rabhi, A. Active disturbance rejection control of flexible industrial manipulator: a MIMO benchmark problem. Eur. J. Control77, 100965. 10.1016/j.ejcon.2024.100965 (2024).

Xue, J. et al. Speed Tracking Control of High-Speed Train based on particle swarm optimization and adaptive Linear active disturbance rejection control. Appl. Sci.12, 10558 (2022).

Zhang, M. et al. Bus Voltage Control of Photovoltaic Grid Connected Inverter based on adaptive Linear active disturbance rejection. Energies15, 5556 (2022).

Liu, C., Cheng, Y., Liu, D., Cao, G. & Lee, I. Research on a LADRC strategy for trajectory tracking control of delta high-speed parallel robots. Math. Probl. Eng.2020, 1–12 (2020).

Xiao-jun, M., Qing-han, Z., Dong, Y. & Shu-guang, W. Speed tracking of PMSM drive for hybrid electric vehicle based on LADRC, in: 2014 IEEE Conf. Expo Transp. Electrif. Asia-Pacific (ITEC Asia-Pacific), IEEE, pp. 1–4. (2014).

Liu, K. et al. Secondary frequency control of isolated microgrid based on LADRC. IEEE Access.7, 53454–53462 (2019).

Zhang, M. et al. MPPT Control Algorithm Based on Particle Swarm Optimization and adaptive Linear active disturbance rejection control. Energies15, 9091 (2022).

Wei, L., Zhou, Z., Wang, B. & Fang, F. ADRC-based control strategy for DC‐link voltage of flywheel energy storage system. Energy Sci. Eng.11, 4141–4154 (2023).

Wolpert, D. H. & Macready, W. G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput.1, 67–82 (1997).

Li, L., Zhao, W., Wang, H., Xu, Z. & Ding, Y. Sand cat swarm optimization based maximum power point tracking technique for photovoltaic system under partial shading conditions. Int. J. Electr. Power Energy Syst.161, 110203 (2024).

Aljanabi, M. & Al-shamani, A. N. Modified Track. Mechanism Horse Optim. 060008 (2024).

Elmanakhly, D. A., Saleh, M., Rashed, E. A. & Abdel-Basset, M. Efficient binary horse herd optimization method for feature selection: analysis and validations. IEEE Access.10, 26795–26816 (2022).

Hosseinalipour, A. & Ghanbarzadeh, R. A novel approach for spam detection using horse herd optimization algorithm. Neural Comput. Appl.34, 13091–13105 (2022).

Zeddini, M. A., Krim, S. & Mimouni, M. F. Experimental validation of an advanced metaheuristic algorithm for maximum power point tracking of a shaded photovoltaic system: a comparative study between three approaches. Energy Rep.10, 161–185 (2023).

Ibrahim, A. W. et al. A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions. Heliyon10, e37458. 10.1016/j.heliyon.2024.e37458 (2024). PubMed PMC

Nzoundja Fapi, C. B., Tchakounté, H., Ndje, M., Wira, P. & Kamta, M. Extraction of the Global Maximum Power for PV system under PSC using an improved PSO technique, period. Polytech. Electr. Eng. Comput. Sci. 1–10. 10.3311/ppee.22254 (2023).

Sundareswaran, K. & Palani, S. Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renew. Energy75, 308–317 (2015).

Li, H., Yang, D., Su, W., Lü, J. & Yu, X. An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Trans. Ind. Electron.66, 265–275 (2018).

Ibrahim, A., Obukhov, S. & Aboelsaud, R. Determination of global maximum power point tracking of PV under partial shading using cuckoo search algorithm. Appl. Sol Energy55, 367–375 (2019).

Eltamaly, A. M., Al-Saud, M. S., Abokhalil, A. G. & Farh, H. M. H. Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading. Renew. Sustain. Energy Rev.124, 109719 (2020).

Singh Chawda, G., Prakash Mahela, O., Gupta, N., Khosravy, M. & Senjyu, T. Incremental conductance based particle swarm optimization algorithm for global maximum power tracking of solar-PV under nonuniform operating conditions. Appl. Sci.10, 4575 (2020).

Hayder, W. et al. A comparative study in MPPT algorithm for PV system control under partial shading conditions. Energies13, 2035 (2020).

Agrawal, P., Asim, M. & Tariq, M. Particle Swarm Optimization (PSO) for Maximum Power Point Tracking, in: 2022 2nd Int. Conf. Emerg. Front. Electr. Electron. Technol., IEEE, pp. 1–5. (2022).

Berttahar, F., Abdeddaim, S., Betka, A. & Omar, C. A comparative study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in partially shaded Photovoltaic systems. Power Electron. Drives9, 86–105. 10.2478/pead-2024-0006 (2024).