Abstract
Photovoltaic (PV) cell (PVC) modeling predicts the behavior of PVCs in various real-world environmental settings and their resultant current–voltage and power–voltage characteristics. Focusing on PVC parameter identification, this study presents an enhanced particle swarm optimization (EPSO) algorithmto accurately and efficiently extract optimal PVC parameters. Specifically, the EPSO algorithm optimizes the minimum mean squared error between measured and estimated data and, on this basis, extractsthe parameters of the single-, double-, and triple-diode models and the PV module. To examine its effectiveness, the proposed EPSO algorithm is compared with other swarm optimization algorithms. The effectiveness of the proposed EPSO algorithm is validated through simulation. In addition, the proposed EPSO algorithm also exhibits advantages such as an excellent optimization performance, a high parameter estimation accuracy, and a low computational complexity.
Highlights
Fossil fuels are one of the energy sources that can best accommodate human needs.Due to their low cost, high efficiency, and ease of transportation, fossil fuels have gained tremendous popularity around the globe, which has resulted in an increase in their use [1].Despite their advantages, fossil fuels inevitably have shortcomings [2,3]
It is worth noting that the convergence values of the enhanced particle swarm optimization (EPSO) algorithm were the lowest of all the algorithms used to identify the parameters of the single-diode model
This finding further demonstrates that the EPSO algorithm outperformed the other algorithms in terms of convergence
Summary
Fossil fuels are one of the energy sources that can best accommodate human needs Due to their low cost, high efficiency, and ease of transportation, fossil fuels have gained tremendous popularity around the globe, which has resulted in an increase in their use [1]. To estimate PVC model parameters, equation-based analytical methods have been proposed [12,13]. These algorithms first randomly generate an initial value and subsequently use a repeated process to describe the variation in the fitness function of the problem with parameters This process is repeated continuously until the fitness function meets the preset termination conditions, at which point satisfactory optimization results are obtained. Andproposes an objective function for addressing PVC model parameter identification and an implementation process based on the EPSO algorithm (Section 3.2).
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