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Xiaodong Wang

Bio: Xiaodong Wang is an academic researcher from Zhejiang Normal University. The author has contributed to research in topics: Particle swarm optimization & Multi-swarm optimization. The author has an hindex of 2, co-authored 2 publications receiving 342 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated currentvoltage characteristics, and the performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models.
Abstract: In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated current-voltage characteristics. The performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models. Based on synthetic and experimental current-voltage data, it has been confirmed that the proposed method can obtain higher parameter precision with better computational efficiency than the GA method. Compared with conventional gradient-based methods, even without a good initial guess, the PSO method can obtain the parameters of solar cells as close as possible to the practical parameters only based on a broad range specified for each of the parameters.

306 citations

Journal ArticleDOI
TL;DR: In this article, a particle swarm optimization (PSO)-based parameter identification technique of proton exchange membrane (PEM) fuel cell models was proposed in terms of the voltage-current characteristics.

115 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a review of the important works on the modelling and parameters estimation of photovoltaic (PV) cells for PV simulation is presented, which provides the concepts, features, and highlights the advantages and drawbacks of three main PV cell models, namely the single diode RS-, RP- and the two-diode.

466 citations

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TL;DR: The Chaotic Whale Optimization Algorithm (CWOA) is proposed, using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm for the parameters estimation of solar cells.

465 citations

Journal ArticleDOI
TL;DR: Comparative study among different parameter estimation techniques is presented to show the effectiveness of the developed approach, and statistical analyses are carried out to measure the accuracy of the estimated parameters and model suitability.

426 citations

Journal ArticleDOI
TL;DR: In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed based on the conducted review, some recommendations for future research are provided.
Abstract: The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

419 citations

Journal ArticleDOI
01 Aug 2014-Energy
TL;DR: The ABC (artificial bee colony) algorithm is proposed, an evolutionary method inspired by the intelligent foraging behavior of honey bees, which exhibits a better search capacity to face multi-modal objective functions in comparison with other evolutionary algorithms.

353 citations