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IMPROVED MAXIMUM POWER POINT TRACKING FOR SOLAR PV SYSTEMS UNDER PARTIAL SHADING USING GREY WOLF OPTIMIZATION BASED INCREMENTAL CONDUCTANCE METHOD

AUTHORS:
Chinta Mahesh
Dr. A. Srinivasa Reddy
Balla Haveela
Krishnapuram Hemanth
Mentor
P.Lakshmi Prasanna
Affiliation
Dept. of EEE, Sir C R Reddy College of Engineering, Eluru, India
DOI: 10.55041/ijsmt.v2i4.449
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Maximum Power Point Tracking (MPPT) is essential for improving the efficiency of solar photovoltaic (PV) systems. Under partial shading conditions (PSC), multiple power peaks occur in the power–voltage (P–V) characteristic curve, making it difficult for conventional methods such as Incremental Conductance (INC) to track the global maximum power point (GMPP). This paper proposes an improved MPPT technique by combining Grey Wolf Optimization (GWO) with the INC method. The GWO algorithm is used to locate the global peak, while the INC method ensures fast and accurate tracking of the maximum power point. The proposed GWO-INC approach reduces tracking time, minimizes power loss, and improves overall system efficiency. Simulation results demonstrate that this method performs better than traditional MPPT techniques under varying environmental conditions.


Index Terms—MPPT, Solar Photovoltaic Systems, Partial Shading Conditions (PSC), Grey Wolf Optimization (GWO), Incremental Conductance (INC) Method, Hybrid MPPT Algorithm, Global Maximum Power Point (GMPP), Swarm Intelligence, Metaheuristic Algorithms, DC-DC Converters.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 25 2026
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Mahesh, C., Reddy, A. S., Haveela, B. & Hemanth, K. (2026). Improved Maximum Power Point Tracking for Solar PV Systems under Partial Shading using Grey Wolf Optimization Based Incremental Conductance Method. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.449

Mahesh, Chinta, et al.. "Improved Maximum Power Point Tracking for Solar PV Systems under Partial Shading using Grey Wolf Optimization Based Incremental Conductance Method." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.449.

Mahesh, Chinta,A. Reddy,Balla Haveela, and Krishnapuram Hemanth. "Improved Maximum Power Point Tracking for Solar PV Systems under Partial Shading using Grey Wolf Optimization Based Incremental Conductance Method." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.449.

References
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2.H. Ibrahim and S. R. Aslan, "Solar photovoltaic water pumping system approach for electricity generation and irrigation: Review," Diagnostyka, vol. 24, no. 2, pp. 1–7,2023.

3.Verma, A. Alam, A. Sarwar, M. Tariq et al., "Meta-heuristic optimization techniques used for maximum power point tracking in solar PV system," Electronics, vol. 10, no. 19, p. 2419, Oct. 2021.

4.Kumar, "Performance analysis of perturb & observe and incremental conductance method of MPPT in solar PV-based power generation," Renewable Power for Sustainable Growth, vol. 1086, 2024.

5.I.-M. Zakki, M. N. M. Hussain, and N. Seroji, "Implementation of particle swarm optimization for tuning of PID controller in Arduino nano for solar MPPT system," J. Elect. Electron. Syst. Res. (JEESR), vol. 13, pp. 78–85, Jan. 2018.

6.Fei and C. Lu, "Adaptive sliding mode control of dynamic systems using double loop recurrent neural network structure," IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 4, pp. 1275–1286, Apr. 2018.

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8.A. Khan, L. Khan, S. Ahmad et al., "RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system," PLoS ONE, vol. 16, no. 4, Apr. 2021.

9.Dehghani, M. Taghipour, G. B. Gharehpetian, and M. Abedi, "Optimized fuzzy controller for MPPT of grid- connected PV systems in rapidly changing atmospheric conditions," J. Modern Power Syst. Clean Energy, vol. 9, no. 2, pp. 376–383, Mar. 2021.

10.Priyadarshi, S. Padmanaban, J. B. Holm-Nielsen et al., "An experimental estimation of hybrid ANFIS-PSO-based MPPT for PV grid integration under fluctuating sun irradiance," IEEE Syst. J., vol. 14, no. 1, pp. 1218–1229, Mar. 2020.

 
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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