Authors:
Omkar Tripathy,Maheswar Prasad Behera,Litu Kumar Samal,Nithya Palanivel,Jeyanthi Sivasubramanian,Bibhu Prasad Ganthia,DOI NO:
https://doi.org/10.26782/jmcms.2025.12.00009Keywords:
Photovoltaic Systems,MPPT,Seagull Optimization Technique,High-Efficiency Power Extraction,High-Gain Quadratic Boost Converter ,Abstract
Maximum Power Point Tracking (MPPT) techniques is efficient technique implemented high photovoltaic power generation in modern power system. This paper will present a MPPT strategy with a Seagull Optimization Algorithm (SOA)-based strategy and high-gain Voltage-Multiplier Coupled Quadric Boost Converter to implement a high-efficiency power extraction in PV systems. The SOA takes advantage of the hunting nature of seagulls so that the operating point of the PV array can be optimised and that the global maximum power point can be reached within seconds even in dynamic irradiance and temperature conditions. Combining this smart MPPT approach with a high-gain quadratic boost converter can achieve large voltage step-up on low PV input to decrease converter stress and increase energy harvesting. Through simulation, the proposed method proves to have a higher tracking speed, efficiency, and stability relative to existing ones (Perturb and Observe) (P&O) and Incremental Conductance (IncCond). The SOA-based MPPT is able to effectively prevent local maxima under partial shading conditions to generate optimal power extraction. The offered system demonstrates the high increase in the general energy efficiency, and it can be applied to both grid-connected and stand-alone PV applications. This combination of smart optimization and sophisticated converter design offers a potential remedy on the extraction of the best performance of a PV system under real operating conditions.Refference:
I. Al-Samawi, A. A., Atiyah, A. S., & Al-Jrew, A. H. (2025). Power Optimization of Partially Shaded PV System Using Interleaved Boost Converter-Based Fuzzy Logic Method. Eng, 6(8), 201. 10.3390/eng6080201
II. A.S. Valarmathy, M. Prabhakar. (2024). High gain interleaved boost-derived DC-DC converters – A review on structural variations, gain extension mechanisms and applications. e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 8, 2024, 100618, ISSN 2772-6711. 10.1016/j.prime.2024.100618
III. B. P. Ganthia, R. Pradhan, S. Das and S. Ganthia, “Analytical study of MPPT based PV system using fuzzy logic controller,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 3266-3269. 10.1109/ICECDS.2017.8390063.
IV. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 2138-2142. 10.1109/ICEEOT.2016.7755068.
V. Chakole, N., Remamany, K. P., Mohan, G., Sasirekha, P., Kumar, N. M. G., Kumar, C. R., … & Ganthia, B. P. (2025). Optimal Energy Management for Hybrid PV-Wind-Battery Microgrids through Markov Decision Processes Technique. International Journal of Smart Grid-ijSmartGrid, 9(3), 127-145.
VI. Chalh, A., chaibi, R., Hammoumi, A.E. et al. (2022). A novel MPPT design based on the seagull optimization algοrithm for phοtovοltaic systems operating under partial shading. Sci Rep 12, 21804. 10.1038/s41598-022-26284-x
VII. Chikezie M. Emeghara, Satish M. Mahajan, Ali Arzani, Two-stage photovoltaic system with a high-gain fifth-order boost converter, e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 13, 2025, 101038, ISSN 2772-6711. 10.1016/j.prime.2025.101038
VIII. Ganthia, B. P., Panda, S., Remamany, K. P., Chaturvedi, A., Begum, A. Y., Mohan, G., … & Ishwarya, S. (2025). Experimental techniques for enhancing PV panel efficiency through temperature reduction using water cooling and colour filters. Electrical Engineering, 1-27.
IX. G.Veera Sankara Reddy, S. Vijayaraj, Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms, Franklin Open, Volume 11, 2025, 100291, ISSN 2773-1863, 10.1016/j.fraope.2025.100291
X. Himani Daulat, Krishna Chauhan & Tarun Varma. (2025). Empowering quadrature mirror filter bank architectures: dynamic-grey wolf optimization approach for elevated filter orders. Engineering Optimization 57:6, pages 1575-1603.
XI. K Rajaram & R Kannan. (2024). Design and implementation of adaptive pufferfish optimization algorithm based efficient MPPT and DC-DC boost converter for agriculture applications under partial shading conditions. Intelligent Decision Technologies 19:2, pages 1074-1090.
XII. Keerthi Sonam Soma, Balamurugan Ramadoss & Karuppiah Natarajan. (2024). Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems. Recent Advances in Electrical & Electronic Engineering 17:10, pages 1023-1041.
XIII. Marlin S & Sundarsingh Jebaseelan. (2024). A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems. Sustainable Computing: Informatics and Systems 41, pages 100946.
XIV. M. Mohanty, N. Nayak, B. P. Ganthia and M. K. Behera, “Power Smoothening of Photovoltaic System using Dynamic PSO with ESC under Partial Shading Condition,” 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), Bhubaneswar, India, 2023, pp. 675-680. 10.1109/APSIT58554.2023.10201763.
XV. Ming-Wei Li, Rui-Zhe Xu, Zhong-Yi Yang, Wei-Chiang Hong, Xiao-Gang An & Yi-Hsuan Yeh. (2024). Optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm. Applied Soft Computing 152, pages 111197.
XVI. P. K. Sahu, A. Mohanty, B. P. Ganthia and A. K. Panda, “A multiphase interleaved boost converter for grid-connected PV system,” 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), Durgapur, 2016, pp. 1-6. 10.1109/MicroCom.2016.7522539.
XVII. S. J. Rubavathy et al., “Smart Grid Based Multiagent System in Transmission Sector,” 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2021, pp. 1-5. 10.1109/ICIRCA51532.2021.9544644.
XVIII. Shih-Cheng Horng & Shieh-Shing Lin. (2022). Incorporate seagull optimization into ordinal optimization for solving the constrained binary simulation optimization problems. The Journal of Supercomputing 79:5, pages 5730-5758.
XIX. Soham Chakraborty, Amritesh Kumar, A Multilevel Based High Gain Switched Inductor Quadratic DC-DC Boost Converter, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 448-453, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.074
XX. Sourya Kumar Nej, S. Sreejith, Indrojeet Chakraborty, Dual-Output Multistage Switched-Capacitor Quadratic Boost (MSC-QBC) DC-DC Converter for Solar Photovoltaic Application, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 965-970, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.159
XXI. Vimal Kumar Pathak, Swati Gangwar & Mithilesh K. Dikshit. (2025). A Comprehensive Survey on Seagull Optimization Algorithm and Its Variants. Archives of Computational Methods in Engineering 32:6, pages 3651-3685.
XXII. Yancang Li, Weizhi Li, Qiuyu Yuan, Huawang Shi & Muxuan Han. (2023). Multi-strategy Improved Seagull Optimization Algorithm. International Journal of Computational Intelligence Systems 16:1.

