Abstract
Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.
Highlights
Solar power generation has seen rapid growth in the past decade (Madvar et al, 2018; Al-Dahidi et al, 2019; Guozden et al, 2020; Sohani et al, 2021)
Motivation of the research is to combine the advantages of both particle swarm optimization (PSO) and fuzzy control to improve the speed of the maximum power point tracking (MPPT) controller, find a global optimal solution under both partial shading condition (PSC) and uniform irridiation, and keep the simple structure of PSO at the same time
For the first instance of partial shading, the P-V curve is shown in Figure 7, where the peak of the curve is the global maximum power point (MPP)
Summary
Solar power generation has seen rapid growth in the past decade (Madvar et al, 2018; Al-Dahidi et al, 2019; Guozden et al, 2020; Sohani et al, 2021). Soft computing based algorithms were recently developed to obtain the global optimal solution under PSC. Biological optimization algorithms such as genetic algorithms (GA), gray wolf optimization, colony of flashing flies, artificial bee colony, and particle swarm optimization (PSO) have been applied to MPPT under PSC. It results in more consistent solution and simpler control structure. Motivation of the research is to combine the advantages of both PSO and fuzzy control to improve the speed of the MPPT controller, find a global optimal solution under both PSC and uniform irridiation, and keep the simple structure of PSO at the same time. The conclusion and recommendations are made in the last section
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