Simulation Study of a Hybrid MPPT Controller with Enhanced Walrus Optimization and Levenberg-Marquardt Training

Daniel Kwegyir et al.

Abstract

The increasing deployment of photovoltaic (PV) systems demands efficient maximum power point tracking (MPPT) methods to optimize energy extraction under varying environmental conditions. Conventional MPPT algorithms often suffer from slow convergence and instability under fluctuating irradiance and temperature. Here we propose a hybrid MPPT controller combining an enhanced Walrus Optimization algorithm with Vigilante Selection and Juvenile Update (WOVE-NSJ) and Levenberg-Marquardt (LM) training of a feedforward neural network (FNN). This approach improves global search and local fine-tuning, achieving near-ideal tracking efficiencies of 99.98%, reduced average tracking time (0.0914 s), and minimal overshoot with near-instantaneous settling times. Benchmarking against state-of-the-art ANN-based controllers demonstrates superior transient stability and robustness. These findings suggest that the proposed WOVE-NSJ-LM-FNN controller offers a promising solution for real-time, high-performance MPPT in PV systems, enhancing power output and system reliability under dynamic conditions.

Keywords

Walrus Optimization, Levenberg-Marquardt Algorithm, Maximum Power Point Tracking, Feedforward Neural Networks, Photovoltaic Systems

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References

Nations.” Accessed: Oct. 01, 2025. https://www.un.org/en/climatechange/raising-ambition/renewable-energy

“Photovoltaic cells | Research Starters | EBSCO Research.” Accessed: Sep. 01, 2025. https://www.ebsco.com/research-starters/environmental-sciences/photovoltaic-cells

M. Tawalbeh, A. Al-Othman, F. Kafiah, E. Abdelsalam, F. Almomani, and M. Alkasrawi, “Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook,” Science of The Total Environment, vol. 759, p. 143528, Mar. 2021, doi: 10.1016/j.scitotenv.2020.143528.

G. Masson, M. de l’Epine, and I. Kaizuka, “Trends in PV Applications 2024,” Oct. 2024, doi: 10.69766/JNEW6916.

A. El Hammoumi, S. Chtita, S. Motahhir, and A. El Ghzizal, “Solar PV energy: From material to use, and the most commonly used techniques to maximize the power output of PV systems: A focus on solar trackers and floating solar panels,” Energy Reports, vol. 8, pp. 11992–12010, Nov. 2022, doi: 10.1016/j.egyr.2022.09.054.

M. E. B. Ghribi, L. Garcia-Gutierrez, Z. E. T. Ternifi, Z. Zheng, G. Bachir, and M. Aillerie, “Tracking the maximum power point of solar panels through direct estimation of optimum voltage with temperature,” Clean Energy, vol. 8, no. 4, pp. 135–146, Aug. 2024, doi: 10.1093/ce/zkae044.

H. Chen, L. Xiang, L. Lin, and S. Huang, “Fractional-order PID Load Frequency Control for Power Systems Incorporating Thermostatically Controlled Loads,” in Proceedings of 2021 IEEE 4th International Electrical and Energy Conference, CIEEC 2021, Institute of Electrical and Electronics Engineers Inc., May 2021. doi: 10.1109/CIEEC50170.2021.9510621.

A. I. M. Ali and H. R. A. Mohamed, “Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation,” International Journal of Electrical Power & Energy Systems, vol. 137, p. 107805, May 2022, doi: 10.1016/j.ijepes.2021.107805.

K. S. Tey and S. Mekhilef, “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level,” Solar Energy, vol. 101, pp. 333–342, Mar. 2014, doi: 10.1016/j.solener.2014.01.003.

J. Ahmad, “A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays,” in 2010 2nd International Conference on Software Technology and Engineering, IEEE, Oct. 2010. doi: 10.1109/ICSTE.2010.5608868.

N. F. Ibrahim et al., “A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system,” PLoS One, vol. 18, no. 11, p. e0293613, Nov. 2023, doi: 10.1371/journal.pone.0293613.

J. T. Sousa and R. S. Barbosa, “Comparison of Classical and Artificial Intelligence Algorithms to the Optimization of Photovoltaic Panels Using MPPT,” Algorithms, vol. 18, no. 8, p. 493, Aug. 2025, doi: 10.3390/a18080493.

D. Devikanniga, K. Vetrivel, and N. Badrinath, “Review of meta-heuristic optimization based artificial neural networks and its applications,” J Phys Conf Ser, vol. 1362, no. 1, 2019, doi: 10.1088/1742-6596/1362/1/012074.

M. Han, Z. Du, K. F. Yuen, H. Zhu, Y. Li, and Q. Yuan, “Walrus optimizer: A novel nature-inspired metaheuristic algorithm,” Expert Syst Appl, vol. 239, p. 122413, Apr. 2024, doi: 10.1016/j.eswa.2023.122413.

A. Saberi, M. Niroomand, and B. M. Dehkordi, “An Improved P&O Based MPPT for PV Systems with Reduced Steady-State Oscillation,” Int J Energy Res, vol. 2023, pp. 1–15, Jul. 2023, doi: 10.1155/2023/4694583.

M. Nkambule, A. Hasan, and A. Ali, “Proportional study of Perturb & Observe and Fuzzy Logic Control MPPT Algorithm for a PV system under different weather conditions,” in 2019 IEEE 10th GCC Conference & Exhibition (GCC), IEEE, Apr. 2019, pp. 1–6. doi: 10.1109/GCC45510.2019.1570516142.

J. López Seguel, S. I. Seleme Jr, and L. M. F. Moráis, “Comparison of the performance of MPPT methods applied in converters Buck and Buck-Boost for autonomous photovoltaic systems,” Ingeniare. Revista chilena de ingeniería, vol. 29, no. 2, pp. 229–244, Jun. 2021, doi: 10.4067/S0718-33052021000200229.

H. A. Sher, A. F. Murtaza, A. Noman, K. E. Addoweesh, and M. Chiaberge, “An intelligent control strategy of fractional short circuit current maximum power point tracking technique for photovoltaic applications,” Journal of Renewable and Sustainable Energy, vol. 7, no. 1, Jan. 2015, doi: 10.1063/1.4906982.

D. Baimel, S. Tapuchi, Y. Levron, and J. Belikov, “Improved Fractional Open Circuit Voltage MPPT Methods for PV Systems,” Electronics (Basel), vol. 8, no. 3, p. 321, Mar. 2019, doi: 10.3390/electronics8030321.

A. Hassan, O. Bass, and M. A. S. Masoum, “An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems,” Energy Reports, vol. 9, pp. 1535–1548, Dec. 2023, doi: 10.1016/j.egyr.2022.12.088.

M. Z.-E. Masry, A. Mohammed, F. Amer, and R. Mubarak, “New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems,” Sustainability, vol. 15, no. 14, p. 10884, Jul. 2023, doi: 10.3390/su151410884.

Q. Pang, F. Zhang, S. Han, T. Zhou, and Y. Wang, “A new two-stage MPPT technique for enhancing the performance of PV system,” Electrical Engineering, vol. 107, no. 8, pp. 10899–10909, Aug. 2025, doi: 10.1007/s00202-025-03067-x.

I. S. Millah, P. C. Chang, D. F. Teshome, R. K. Subroto, K. L. Lian, and J.-F. Lin, “An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions,” IEEE Open Journal of the Industrial Electronics Society, vol. 3, pp. 392–408, 2022, doi: 10.1109/OJIES.2022.3179284.

M. E. Başoğlu, “A Fast GMPPT Algorithm Based on PV Characteristic for Partial Shading Conditions,” Electronics (Basel), vol. 8, no. 10, p. 1142, Oct. 2019, doi: 10.3390/electronics8101142.

B. Mbarki, F. Fethi, J. Chrouta, and A. Zaafouri, “Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system,” Transactions of the Institute of Measurement and Control, vol. 46, no. 2, pp. 316–325, Jan. 2024, doi: 10.1177/01423312231173022.

B. C. Phan, Y.-C. Lai, and C. E. Lin, “A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition,” Sensors, vol. 20, no. 11, p. 3039, May 2020, doi: 10.3390/s20113039.

S. S. Saad, M. A. Atiqi Mohd Zainuri, and A. Hussain, “Implementation of Maximum Power Point Tracking Techniques for PV-Wind Hybrid Energy System: A Review,” in 2021 International Conference on Electrical Engineering and Informatics (ICEEI), IEEE, Oct. 2021, pp. 1–6. doi: 10.1109/ICEEI52609.2021.9611148.

Y. Li, L. Li, Z. Lian, K. Zhou, and Y. Dai, “A quasi-opposition learning and chaos local search based on walrus optimization for global optimization problems,” Sci Rep, vol. 15, no. 1, p. 2881, Jan. 2025, doi: 10.1038/s41598-025-85751-3.

L. Abualigah et al., “An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering,” Array, vol. 26, p. 100409, Jul. 2025, doi: 10.1016/j.array.2025.100409.

P. Trojovský and M. Dehghani, “Walrus Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm,” Oct. 24, 2022. doi: 10.21203/rs.3.rs-2174098/v1.

P. Rajarajeswari, B. D. V, and A. L. M. S, “Hybrid Termite Queen and Walrus Optimization Algorithm-based energy efficient cluster-based routing with static and mobile sink node in WSNs,” Peer Peer Netw Appl, vol. 18, no. 2, p. 8, Mar. 2025, doi: 10.1007/s12083-024-01848-y.

S. Vujošević, M. Ćalasan, and M. Micev, “Hybrid walrus optimization algorithm techniques for optimized parameter estimation in single, double, and triple diode solar cell models,” AIP Adv, vol. 14, no. 8, Aug. 2024, doi: 10.1063/5.0223492.

J. He, Y. Tang, X. Guo, H. Chen, and W. Guo, “Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization,” Sci Rep, vol. 14, no. 1, p. 11981, May 2024, doi: 10.1038/s41598-024-62722-8.

Y. Ke, L. Shi, W. Ji, P. Luo, and L. Guo, “Application of the Multi-Strategy Improved Walrus Optimization Algorithm in Mobile Robot Path Planning,” IEEE Access, vol. 12, pp. 184216–184229, 2024, doi: 10.1109/ACCESS.2024.3506977.

S. Lv, J. Zhuang, Z. Li, H. Zhang, H. Jin, and S. Lü, “An enhanced walrus optimization algorithm for flexible job shop scheduling with parallel batch processing operation,” Sci Rep, vol. 15, no. 1, p. 5699, Feb. 2025, doi: 10.1038/s41598-025-89527-7.

Y. Geng, Y. Li, and C. Deng, “An Improved Binary Walrus Optimizer with Golden Sine Disturbance and Population Regeneration Mechanism to Solve Feature Selection Problems,” Biomimetics, vol. 9, no. 8, p. 501, Aug. 2024, doi: 10.3390/biomimetics9080501.

H. M. Hasanien, I. Alsaleh, Z. Ullah, and A. Alassaf, “Probabilistic optimal power flow in power systems with Renewable energy integration using Enhanced walrus optimization algorithm,” Ain Shams Engineering Journal, vol. 15, no. 3, p. 102663, Mar. 2024, doi: 10.1016/j.asej.2024.102663.

A. Dev et al., “Improved Extraction of Parameters of Solar PV Cell Diode Model Using Marine Walrus Inspired Optimization Algorithm,” IEEE Access, vol. 12, pp. 181217–181231, 2024, doi: 10.1109/ACCESS.2024.3504559.

S. E. V. S. Pillai, G. S. Nadella, K. Meduri, N. A. Priyadharsini, A. Bhuvanesh, and D. Kumar, “A walrus optimization-enhanced long short-term memory model for credit fraud detection in banking,” International Journal of Information Technology, May 2025, doi: 10.1007/s41870-025-02574-1.

V. K. R. M and A. C, “Secure Clustering and Routing Based Multi Objective Walrus Optimization Algorithm for Wireless Sensor Networks,” International Journal of Electrical and Electronics Engineering, vol. 12, no. 1, pp. 211–221, Jan. 2025, doi: 10.14445/23488379/IJEEE-V12I1P121.

J. Li, R. Lu, B. Zeng, J. Zhang, Y. Deng, and H. Feng, “Multi-threshold medical image segmentation based on the enhanced walrus optimizer,” J Supercomput, vol. 81, no. 4, p. 513, Feb. 2025, doi: 10.1007/s11227-025-07023-1.

A. Kumar and R. Agarwal, “Mathematical Modeling and Analysis of Single-Diode, Double-Diode and Triple Diode based PV Module,” in 2023 International Conference for Advancement in Technology, ICONAT 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICONAT57137.2023.10080741.

I. Haseeb et al., “Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm,” Applied Sciences, vol. 11, no. 23, p. 11332, Nov. 2021, doi: 10.3390/app112311332.

J. Zupan, “Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*,” 1994.

DOI: http://dx.doi.org/10.47238/ijeca.v10i2.289

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