An efficient fault diagnosis method for PV systems based on operating voltage-window

Gökmen, Nuri; Karatepe, Engin; Silvestre, S.; Celik, Berk; Ortega, Pablo

doi:10.1016/j.enconman.2013.05.015

Cited by 97 publications

(30 citation statements)

References 30 publications

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“…In a non-uniform temperature distribution, PV cell in a string operates at different power points and results in a faulty mode thereby reducing the performance of PV with reduced efficiency (Gokmen et al, 2013) [605][606][607][608][609][610][611][612][613][614][615][616][617] Contents lists available at ScienceDirect Solar Energy j o u r n a l h o m e p a g e : w w w . e l s e v i e r .…”

Section: Introductionmentioning

confidence: 99%

Experimental performance evaluation and modeling of jet impingement cooling for thermal management of photovoltaics

Bahaidarah

2016

Solar Energy

139

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Section: Introductionmentioning

confidence: 99%

Experimental performance evaluation and modeling of jet impingement cooling for thermal management of photovoltaics

Bahaidarah

2016

Solar Energy

139

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“…In [23], a statistical outlier detection method is employed. In [24], the expected output voltage value for different MPP is calculated and is used as a reference value for fault detection.…”

Section: A Methods 1: Direct Difference Measurement (Ddm) Between Expmentioning

confidence: 99%

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Akram

Lotfifard

2015

IEEE Trans. Sustain. Energy

142

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In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short-and opencircuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturer's datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method. Index Terms-Fault detection, monitoring systems, photovoltaic (PV) modeling, probabilistic neural network (PNN).

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“…In comparison with the three aforementioned fault detection methods, electrical methods based on statistical signal and processing approaches [6][7][8], power losses analysis [9][10][11], I-V characteristic analysis [12][13][14], and voltage-current measurements [15][16][17][18][19][20] are found to be more advantageous and promising [4]. These electrical methods can be used to investigate numerous faults in PV systems such as short-circuit faults, open-circuit faults, shading conditions, degradation faults, the line to line faults, bypass diode faults, DC arc faults, bridging faults, MPPT errors, and others.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification

et al. 2019

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In this paper, photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN) are presented. Simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules is proposed. The proposed method is verified using the developed health monitoring system which is installed in a grid-connected PV system. Each of the PV modules is monitored via WSN to detect any individual faulty module. The analysis of PV string failure includes several electrical fault scenarios and their impact on the PV string characteristics. The results show that a degraded or faulty module exhibits low operating voltage as compared to the normal module. The developed health monitoring system also includes a graphical user interface (GUI) program which graphically displays the real-time operating voltage of each module with colors and thus helping users to identify the faulty modules easily. The faulty modules identification approach is further validated using the PV module electroluminescence (EL) imaging system.

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An efficient fault diagnosis method for PV systems based on operating voltage-window

Cited by 97 publications

References 30 publications

Experimental performance evaluation and modeling of jet impingement cooling for thermal management of photovoltaics

Experimental performance evaluation and modeling of jet impingement cooling for thermal management of photovoltaics

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification

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