Fault classification for Photovoltaic Modules Using Thermography and Machine Learning Techniques

Kurukuru, Varaha Satya Bharath; Haque, Ahteshamul; Khan, Mohammed Ali; Tripathy, Arun Kumar

doi:10.1109/iccisci.2019.8716442

Cited by 52 publications

(36 citation statements)

References 11 publications

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“…The AC faults include gating and switching failures, open and short circuit switches [4], and filter failure-inducing harmonics in the circuits. Whereas DC faults include various module-based faults [5], failure of maximum power-point tracking (MPPT) algorithms, and faults associated with DC-DC converters [6,7]. Also, MPPT systems are responsible for injecting maximum power into the circuit.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Classification Approach for Photovoltaic Systems

et al. 2020

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Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.

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Section: Introductionmentioning

confidence: 99%

A Novel Fault Classification Approach for Photovoltaic Systems

et al. 2020

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“…One way to perform fault classification, which has been receiving increasing attention and popularity in recent literature [9], is the use of artificial intelligence models, especially machine learning classifiers, which is also the main approach that is proposed in this work. In [10], for instance, the use of artificial neural networks to classify the operation of a photovoltaic system in four possible states (normal, degradation, short-circuit, and shadowing) is presented.…”

Section: Fault Classificationmentioning

confidence: 99%

“…One way to achieve that is to include a MS in the PV plant that measures electrical and meteorological variables, manages plant operations (e.g., remote access), detects malfunctions and errors, and reports performance and benchmarking, locally or remotely, through a communication system to the grid operator [ 6 , 7 ]. However, only the MS is not enough to completely solve the problem [ 8 ], since PV faults demand specific techniques to detect and classify them, using monitored data [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

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“…PV faults are categorized into two different PV configurations, including SP and TCT, through neural networks with high accuracy of 99.6%, which is not achieved in the previous research works. Thermal imaging for feature extraction is used in [17] with NN as a classifier for fault detection in PV modules, which achieved 92.8 % overall accuracy as a fault classifier. A comparison of NN classifier with conventional classifiers like K-nearest neighbor (KNN), and support vector machine (SVM) is also made in [17].…”

Section: Introductionmentioning

confidence: 99%

“…Thermal imaging for feature extraction is used in [17] with NN as a classifier for fault detection in PV modules, which achieved 92.8 % overall accuracy as a fault classifier. A comparison of NN classifier with conventional classifiers like K-nearest neighbor (KNN), and support vector machine (SVM) is also made in [17]. Classifiers like SVM and KNN achieved an accuracy of 80.3% and 56.8% respectively, while NN classifier performed better as a fault classifier and achieved an overall accuracy of 92.8% in classification of faults in the PV module.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Fault Categorization in Thin-Film and Crystalline PV Arrays Through Multilayer Neural Network Algorithm

et al. 2020

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Categorization of PV faults is an essential task for improving the efficiency and reliability of a photovoltaic (PV) system. Output characteristics of a solar (PV) system can be severely affected under various fault conditions including short circuit, module mismatch, open circuit, and multiple faults under shading conditions. Such PV faults can potentially be analyzed through the PV characteristic curve analysis using a multilayer neural network with a scaled conjugate gradient algorithm (SCG). This paper presents an extensive investigation for categorization, i.e., classification of the above-mentioned PV faults using the SCG algorithm. The major contribution of the presented research work is the categorization of PV faults in sixteen different classes considering polycrystalline and thin-film PV technologies with two different configurations, including SP and TCT. The fault classification is achieved with high accuracy of 99.6% and a fast-computational time of 0.08 sec. The results are validated through the plot of the Confusion Matrix and Region of Convergence (ROC) with their performance evaluation in MATLAB. The achieved accuracy and fast computational time prove the effectiveness of the multilayer neural network-based approach for classification of the PV faults to increase power output, efficiency, and lifespan of PV systems. INDEX TERMS PV faults, PV technologies, PV characteristics, neural networks.

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Fault classification for Photovoltaic Modules Using Thermography and Machine Learning Techniques

Cited by 52 publications

References 11 publications

A Novel Fault Classification Approach for Photovoltaic Systems

A Novel Fault Classification Approach for Photovoltaic Systems

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Modeling and Fault Categorization in Thin-Film and Crystalline PV Arrays Through Multilayer Neural Network Algorithm

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