Characteristic output of PV systems under partial shading or mismatch conditions

Bai, Jianbo; Cao, Yang; Hao, Yuzhe; Zhang, Zhen; Liu, Sheng; Cao, Fei

doi:10.1016/j.solener.2014.09.048

Cited by 158 publications

(75 citation statements)

References 21 publications

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“…During operation, the photovoltaic arrays may be totally or partially shaded, which can lead to a significant decrease in performance. One of the most influential factors on PV power generation is shade (Bai et al, 2015). Intermittent shadows are caused by rapid clouds or built-in obstructions that are more difficult to predict with intermittent impact on losses (Bressan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Faye¹,

Ndiaye²,

Kobor³

et al. 2017

Int. J. Phys. Sci.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Faye¹,

Ndiaye²,

Kobor³

et al. 2017

Int. J. Phys. Sci.

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“…In previous work [25 & 27] an estimation of the thermal voltage of a PV model under partial shading conditions has been expressed by (3).…”

Section: Equivalent Thermal Voltage (V Te )mentioning

confidence: 99%

“…The results shows that the average rate of change in the output power during irradiance transitions is around 3%, where the maximum rate of change is approximate to 75%. Furthermore, an accurate approach method to simulate the characteristics output of a PV systems under either partial shading or mismatch conditions is proposed by J. Bai et al [3]. The method is using the analysis of the current-voltage (I-V) and power-voltage (P-V) curves for various PV systems.…”

Section: Introductionmentioning

confidence: 99%

Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions

et al. 2017

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The goal of this paper is to model, compare and analyze the performance of multiple photovoltaic (PV) array configurations under various partial shading and faulty PV conditions. For this purpose, a multiple PV array configurations including series (S), parallel (P), series-parallel (SP), total-cross-tied (TCT) and bridge-linked (BL) are carried out under several partial shading conditions such as, increase or decrease in the partial shading on a row of PV modules and increase or decrease in the partial shading on a column of PV modules. Additionally, in order to test the performance of each PV configuration under faulty PV conditions, from 1 to 6 Faulty PV modules have been disconnected in each PV array configuration. Several indicators such as short circuit current (I sc ), current at maximum power point (I mpp ), open circuit voltage (V oc ), voltage at maximum power point (V mpp ), series resistance (R s ), fill factor (FF) and thermal voltage (V te ) have been used to compare the obtained results from each partial shading and PV faulty condition applied to the PV system. MATLAB/Simulink software is used to perform the simulation and the analysis for each examined PV array configuration.

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“…Faults affect a PV system's performance and reliability. The causes of PV system faults are mainly temperature fault, partial shading fault, modules aging, the open-circuit or short-circuit of PV modules, and cell damage [2][3][4]. Temperature faults mainly stem from the PV modules' high surface temperature after absorbing sunlight in contrast to PV array partial shading faults, which are either unpreventable but fluctuating, like clouds, or eliminable, such as fallen leaves and dust.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

et al. 2017

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This paper proposes a heuristic triple layered particle swarm optimization-backpropagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (V oc ), short-circuit current (I sc ), maximum power (P m ) and voltage at maximum power point (V m ) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.

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Characteristic output of PV systems under partial shading or mismatch conditions

Cited by 158 publications

References 21 publications

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Evaluation of the impact of partial shading and its transmittance on the performance of crystalline silicon photovoltaic modules

Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions

A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

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