Journal Article10.1016/J.APENERGY.2017.05.029
Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm
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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.
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About: This article is published in Applied Energy. The article was published on 15 Aug 2017. The article focuses on the topics: Photovoltaic system & Solar energy.
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Citations
Optical Network Unit Placement in Fiber-Wireless (fiwi) Access Network by Whale Optimization Algorithm
Puja Singh,Shashi Prakash +1 more
TL;DR: This study proposes a Whale Optimization Algorithm (WOA) for optimal Optical Network Unit (ONU) placement in Fiber-Wireless (FiWi) access networks, outperforming Greedy and Moth Flame Optimization algorithms in terms of cost efficiency, convergence rate, and time complexity.
Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm
Wei-Lun Yu,Chen-Kai Wen,En-Jui Liu,J. Chang +3 more
Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem
TL;DR: This paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms by hybridization with evolution of the Whale Optimization Algorithm.
Tent chaos and nonlinear convergence factor whale optimization algorithm
Huanlong Zhang,Youmei Pan,Jie Zhang,Kejie Dai,Yuxing Feng +4 more
TL;DR: A Tent chaos and nonlinear convergence factor-enhanced Whale Optimization Algorithm (TWOA) is proposed to improve convergence speed and balance exploration and development, outperforming the original WOA in 10 standard test functions with improved mean and standard deviation.
Symmetric chaotic gradient-based optimizer algorithm for efficient estimation of PV parameters
TL;DR: In this paper , the authors proposed an improved optimization algorithm by combining the gradient-based optimizer with the symmetric chaotic sequence generator to estimate the PV parameters of solar cells and modules, which achieved the lowest root mean square error (RMSE) and standard deviation (SD) values for almost all of the PV cells/modules considered in this paper when compared to some state-of-the-art algorithms.
References
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
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Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays
TL;DR: In this article, the authors proposed a method of modeling and simulation of photovoltaic arrays by adjusting the curve at three points: open circuit, maximum power, and short circuit.
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TL;DR: In this article, a nonlinear least-squares optimization algorithm based on the Newton model modified with Levenberg parameter is described for the extraction of the five illuminated solar cell parameters from the experimental data.
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Parameter estimation of solar photovoltaic (PV) cells: A review
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.
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