International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-13 Issue-138 May-2026
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Performance evaluation of fuzzy logic–based MPPT for a hybrid solar–biomass power system using a Cuk–SEPIC converter

Vineeth Kumar P K1, Jijesh J J1, Ramya P1, Lakshmi Manasa B1 and Niranjana C2

Sri Venkateshwara College of Engineering,VTU, Bengaluru,Karnataka,India1
Department of Electronics and Communication Engineering,Dayananada Sargar University, Bengaluru,Karnataka,India2
Corresponding Author : Vineeth Kumar P K

Recieved : 21-September-2024; Revised : 19-April-2026; Accepted : 04-May-2026

Abstract

The energy sector faces significant challenges in balancing electricity supply and demand, addressing fossil fuel depletion, and mitigating global warming. To achieve sustainability, renewable energy sources (RES) must be effectively utilized. Among RES options such as wind, biomass, and tidal energy, solar energy is particularly notable due to its modularity, cost-effectiveness, and space efficiency. However, the performance of solar photovoltaic (SPV) systems is adversely affected by rapid variations in atmospheric conditions and the occurrence of partial shading conditions (PSC). Under such conditions, conventional maximum power point tracking (MPPT) algorithms may exhibit reduced efficiency. To address this limitation, an artificial intelligence (AI)-based MPPT algorithm is proposed. Among various AI-based MPPT techniques, fuzzy logic maximum power point tracking (FL-MPPT) has demonstrated superior performance in terms of tracking speed and efficiency. To further enhance SPV system reliability in regions with intermittent solar radiation, a power electronic interface (PEI) is designed to integrate biomass as a complementary RES, selected for its availability in rural areas. The proposed hybrid system, combining SPV and biomass energy sources, employs a Cuk–single-ended primary-inductor converter (Cuk–SEPIC) to improve overall reliability and power conversion efficiency. The integration of these energy sources is achieved through the PEI, while the FL-MPPTalgorithm is implemented to optimize maximum power tracking. The system is modeled and evaluated in MATLAB/Simulink, achieving an efficiency of 96% with a tracking time of 0.2 seconds. The results also confirm the superiority of the FL-MPPTapproach over conventional MPPT algorithms. This study presents a hybrid renewable power generation (HRPG) system that effectively integrates SPV and biomass energy sources to mitigate the impacts of atmospheric variations and PSC. The system’s reliability and tracking efficiency are significantly enhanced through the incorporation of the Cuk–SEPIC converter and the FL-MPPTalgorithm. This approach provides a viable solution for rural electrification and demonstrates the potential of AI-based techniques to outperform traditional MPPT methods, thereby advancing sustainable energy solutions for underserved communities.

Keywords

Solar photovoltaic (SPV), Maximum power point tracking (MPPT), FL-MPPT, Hybrid renewable power generation (HRPG), Cuk–SEPIC converter, Biomass energy.

Cite this article

P K VK, JJ Jijesh, P R, B LM, C N. Performance evaluation of fuzzy logic–based MPPT for a hybrid solar–biomass power system using a Cuk–SEPIC converter. International Journal of Advanced Technology and Engineering Exploration. 2026;13(138):791-813. DOI : 10.19101/IJATEE.2024.111101725

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