Parameter determination of Photovoltaic Cells from field testing data using particle swarm optimization

Qin, Hengsi; Kimball, Jonathan W.

doi:10.1109/peci.2011.5740496

Cited by 40 publications

(30 citation statements)

References 9 publications

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“…In general, by introducing a virtual "time" of t = x, the static relationship between two variables y and x can be regarded as dynamics from the linear system governed by (23). Once α i and β i are determined from system identification, diode model parameters I L , I oi , a i and R sh can be solved linearly from (24)- (26).…”

Section: B Multi-diode Modelmentioning

confidence: 99%

A Linear Identification of Diode Models from Single $I$– $V$ Characteristics of PV Panels

Lim

Ye²,

et al. 2015

IEEE Trans. Ind. Electron.

111

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Abstract-This paper presents a novel approach on diode model parameters identification from the I-V characteristics of PV panels. Other than the prevailing methodology of solving a group of nonlinear equations from a few points on the I-V curve, the proposed one views the diode model as the equivalent output of a dynamic system. From this new viewpoint, diode model parameters are linked to the transfer function (after Laplace transform) of the same dynamic system whose parameters are then identified by a simple integral-based linear square. Indoor flash test shows the accuracy and effectiveness of the proposed method, and outdoor module testing shows its ability of online monitoring and diagnostics. Comparisons to the methods of Lambert W function and evolution algorithms are also included.Index Terms-Diode model, I-V characteristics, linear least square, binary search algorithm.

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Section: B Multi-diode Modelmentioning

confidence: 99%

A Linear Identification of Diode Models from Single $I$– $V$ Characteristics of PV Panels

Lim

Ye²,

et al. 2015

IEEE Trans. Ind. Electron.

111

View full text Add to dashboard Cite

show abstract

“…Such processes are often inspired by biological mechanisms of evolution. Some of the popular Evolutionary algorithm like Genetic algorithms (GAs) [6,33], Particle Swarm Optimization (PSO) [7,34,35], Simulated annealing (SA) [11,36], Artificial bee colony (ABC) [37,38], Mimetic algorithm, Cuckoo search [39,40], Bacterial Forging Optimization (BFO) [10,41] etc. were applied to extract the parameter of solar cell.…”

Section: Cmentioning

confidence: 99%

A Review extraction of solar cell modelling parameters

Tamrakar¹,

Gupta²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:Energy from the sun is the best option for electricity generation as it is abundantly available everywhere and sustainable source. Advance of Photovoltaic (PV) technology in recent years has made solar energy one of the practical alternative energy sources available in the energy market. In order to make the PV energy more affordable and cost effective, major focus of the research community and industry is improvement on power efficiency of PV systems. Parameter extraction of solar cell plays a crucial role in the simulation and design calculation of PV system. The circuit model of a solar cell is non-linear and transcendental. Different methods under the categories of analytic, iterative, and evolutionary computational have been proposed to extract the parameters of solar cell in recent years. This paper presents a survey on parameter extraction of solar cell model and its recent advances.

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“…Soon and Low identified the single diode KC65T PV model given by three unknown electrical components, which were optimized by the PSO algorithm based upon log barrier constraint [11]. These unknown electrical components have been identified by Qin and Kimball from field test data using PSO algorithm in which both total solar irradiance and environmental temperature variations are taken into account [12]. These parameters have been identified from combining the GA by the Interior-Point Method IPM by Dizqah et al [13].…”

Section: Introductionmentioning

confidence: 99%

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Bechouat

Younsi

Sedraoui

et al. 2017

Int J Energy Environ Eng

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Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms; genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

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Parameter determination of Photovoltaic Cells from field testing data using particle swarm optimization

Cited by 40 publications

References 9 publications

A Linear Identification of Diode Models from Single $I$– $V$ Characteristics of PV Panels

A Linear Identification of Diode Models from Single $I$– $V$ Characteristics of PV Panels

A Review extraction of solar cell modelling parameters

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

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