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Chef-Based Optimization Algorithm (CBOA)-Based Global Peak Power Tracking Scheme for PV System
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DOI：10.1109/ACCESS.2025.3606338.png)
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Abstract
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Environmental concerns are driving the demand for clean and renewable energy sources. Among renewable sources, the usage of Photovoltaic (PV) systems has been steeply growing in recent decades. Numerous factors, including temperature, shade percentage, and insolation, affect how well the PV system performs. The intermediate converter between solar panels and load adjusts the difference between PV output and load voltage due to variations in isolation and temperature levels. Shading on the solar panel, caused by natural or unnatural reasons, leads to technical issues by creating numerous peaks in the P-V characteristics. One of the several peaks is referred to as Global Peak Power (G ${}_{\mathrm {PP}}$ ), while the others are classified as Local Peak Power (L ${}_{\mathrm {PP}}$ ). The presence of several peaks and the possibility of trapping at LPP rather than GPP make it difficult to identify GPP throughout the optimization process. Therefore, this paper presents Chef based Optimization Algorithm (CBOA) to track GPP of the PV system. CBOA was developed based on the procedure of learning cooking skills in training courses and offers a much structured approach compared to Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The performance of the tracking algorithm is examined by simulation in MATLAB using a 5S5P configuration. In the simulation studies, the CBOA performance is better compared with PSO and GWO in terms of tracking time and maximum magnitude of power.
Keywords:
Photovoltaic (PV) system
local peak power (LPP)
global peak power (GPP)
chef based optimization algorithm (CBOA)
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3.6
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29.4W
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