A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation

Pei, Tingting; Hao, Xiaohong

doi:10.3390/en12091712

Cited by 67 publications

(45 citation statements)

References 28 publications

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“…The decision region of each rule in three linguistic variable models is shown in Figure 8. (8) The CF determines the size of the decision region of each rule without modifying the membership function area. The decision region of each rule in three linguistic variable models is shown in Figure 8.…”

Section: Fuzzy Rule-based Classificationmentioning

confidence: 99%

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Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Balasubramani¹,

Thangavelu²,

Muniraj³

et al. 2020

Energies

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Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

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Section: Fuzzy Rule-based Classificationmentioning

confidence: 99%

“…Ref. [7,8] provide the current and voltage based faulty indicator to detect the faults in the PV system. [9] heightened the value of the mounting variable of commercial-grade PV panel used in a building-integrated PV system for operating with its designed efficiency.…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Balasubramani¹,

Thangavelu²,

Muniraj³

et al. 2020

Energies

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“…The PV parameters include external parameters (open-circuit voltage Voc, shortcircuit current Isc, the voltage Vm and current Im at the maximum power point), internal parameters (photogenerated current Iph, ideal factor A, parasitic diode saturation current Io, series resistance Rs and shunt resistance Rsh), the I-V curve, and the P-V curve. When a PV module encounters different faults, its internal and external parameters or the slope and shape of the I-V/P-V curves will change [7]- [8]. Therefore, through analyses and derivations, the characteristic expression forms and rules to describe various faults can be found.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, through analyses and derivations, the characteristic expression forms and rules to describe various faults can be found. Pei and Hao [8] investigated a new method to diagnose PV faults by observing and evaluating voltage and current changes. Voltage and current indexes were defined by the voltage Vm and current Im at the maximum power point, the open-circuit voltage Voc, and the short-circuit current Isc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

Gao

Wai

2020

IEEE Access

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Under the background of the large-scale construction of photovoltaic (PV) power stations, it is crucial to discover and solve module failures in time for improving the service life and maintaining the normal operation efficiency of modules. Based on analyzing the difference of I-V curves of PV arrays under different fault states, the I-V curves, temperatures and irradiances are taken as input data, and a fusion model of convolutional neural network (CNN) and residual-gated recurrent unit (Res-GRU) is proposed to identify the PV array fault. This model consists of a 1-dimensional CNN module with a 4-layer structure and a Res-GRU module. It has the advantages of end-to-end fault diagnosis, no manual feature extraction, strong anti-interference ability, and usable in the absence of irradiances and temperatures. Moreover, it can not only identify a single fault (e.g., short circuit, partial shading, abnormal aging, etc.), but also can effectively identify hybrid faults. Experimental results show that the classification accuracy of the proposed method is 98.61%, which is better than the ones of the artificial neural network (ANN), the extreme learning machine with kernel function (KELM), the fuzzy C-mean (FCM) clustering, the residual neural network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, in the absence of temperatures and irradiances, the classification accuracy still reaches 95.23%, which has a broad application prospect in the online fault diagnoses of PV arrays. INDEX TERMS Photovoltaic, fault diagnosis, I-V curve, 1-dimensional convolutional neural network (1-D CNN), residual-gated recurrent unit (Res-GRU)

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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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A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation

Cited by 67 publications

References 28 publications

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

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

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