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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Adebiyi, Abayomi A.
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Performance analysis of grid-tied photovoltaic system under varying weather condition and load Adebiyi, Abayomi A.; Lazarus, Ian J.; Saha, Akshay K.; Ojo, Evans E.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp94-106

Abstract

Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250W/m2 and 1000W/m2. The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load.
Grid-tied photovoltaic system MPPT algorithms performance: comparative analysis Nguefack, Louis Nicase; Akindeji, Kayode Timothy; Adebiyi, Abayomi A.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp317-334

Abstract

Between 2015 and 2024, global solar photovoltaic (PV) capacity rose significantly from 223.204 GW to 1624 GW, contributing to the reduction of greenhouse gas emissions associated with fossil-fuel-based power generation. Solar PV is recognized for its environmental benefits and is increasingly seen as a viable alternative for a long-term sustainable energy supply. However, the power output of PV systems is highly dependent on atmospheric conditions, particularly solar irradiation and temperature, which can cause fluctuations and reduce overall efficiency. To address this, maximum power point tracking (MPPT) techniques are employed to optimize energy extraction under varying environmental conditions. This study presents a comparative analysis of four MPPT algorithms, perturb-and-observe (P&O), incremental conductance (InC), fuzzy logic control (FLC), and artificial neural network (ANN) for grid-tied PV systems using MATLAB/Simulink. Each algorithm was evaluated under dynamic conditions to determine its tracking efficiency and responsiveness. The results show that while conventional methods like P&O and InC are simpler, they are less effective under rapidly changing conditions. FLC demonstrates faster convergence but requires greater computational resources. The intelligent controllers demonstrated superior performance: FLC achieved the highest power output of 1.019×10⁶ W with a corresponding voltage of 1.422×10⁴ V, while the ANN algorithm followed closely with 9.650×10⁵ W and 1.200×10⁴ V, respectively. The comparative insights gained from this analysis offer practical guidance for selecting MPPT controllers in real-world solar energy applications.
Co-Authors Akindeji, Kayode Timothy Lazarus, Ian J. Nguefack, Louis Nicase Ojo, Evans E. Saha, Akshay K.