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
Winner-leading competitive swarm optimizer with dynamic Gaussian mutation for parameter extraction of solar photovoltaic models
TL;DR: In WLCSODGM, two improved components are introduced to remedy the inadequacy of CSO and a dynamic Gaussian mutation operator with stretchable mutation amplitude and adaptive mutation probability is integrated to help individuals jump out of poor local optima.
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Solar Array Fault Detection using Neural Networks
Sunil Rao,Andreas Spanias,Cihan Tepedelenlioglu +2 more
- 06 May 2019
TL;DR: This paper develops a framework for the use of feedforward neural networks for fault detection and identification in Photovoltaic arrays and promises to improve efficiency by detecting and identifying eight different faults and commonly occurring conditions that affect power output in utility scale PV arrays.
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Turbulent Flow of Water-Based Optimization Using New Objective Function for Parameter Extraction of Six Photovoltaic Models
TL;DR: In this paper, the Turbulent Flow of Water-based Optimization (TFWO) was used to estimate the parameters of three traditional solar cell models, namely, Single-Diode Solar Cell Model (SDSCM), Double-diode solar cell Model (DDSCM) and Three-Dioretic Solar Cell model (TDSCM).
A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems
TL;DR: The benchmarking results prove that HS-WOA and HS- WOA+ s’ performance is competitive and better than the various algorithms tested against and had a statistically significant performance with lower computational times.
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Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer.
TL;DR: A meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images with better accuracy than other classification methods.
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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Nonlinear Minimization Algorithm for Determining the Solar Cell Parameters with Microcomputers
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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