Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions

Chen, Zhicong; Chen, Yixiang; Wu, Lijun; Cheng, Shuying; Lin, Peijie

doi:10.1016/j.enconman.2019.111793

Cited by 243 publications

(119 citation statements)

References 63 publications

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“…In order to compare and evaluate the performance of the proposed method, the proposed method is compared with the other five methods used in [15], [21], [25], [28] and [38] from both qualitative and quantitative aspects. The qualitative analytic results are summarized in Table 10, and the quantitative analytic results are listed in Table 11.…”

Section: B Compared With Other Methodsmentioning

confidence: 99%

“…Moreover, the method type and the input data amount are also discussed in Table 10. Note that, one applies the corresponding methods from those works in [15], [21], [25], [28] and [38] to the same dataset used in this study, not the accuracy records from the references. In other words, the training set and the testing set are the same in Table 11 for fair comparisons.…”

Section: B Compared With Other Methodsmentioning

confidence: 99%

“…Moreover, the extreme learning machine with kernel functions was used to identify the normal state, the abnormal aging fault, the short-circuit fault, the partial shading fault, and the open-circuit fault. Chen et al [25] also proposed the use of the deep residual network (ResNet) combined with I-V curves for PV fault diagnoses. The data of I-V curves, temperatures, and irradiances were used as inputs to the model in [25].…”

Section: B Compared With Other Methodsmentioning

confidence: 99%

“…Finally, the AI algorithm is adopted to mine the difference of characteristics under different fault conditions for realizing the fault diagnoses. At present, more advanced AI algorithms have been developed to mine the differences between curves or images actively and recognize them, such as CNN [24], residual network (ResNet) [25], adversarial generative network [26], transfer learning [27] etc. Compared with traditional methods, these methods in [24]- [27] do not require the multi-step processing of the signal and realize the fast diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Compared With Other Methodsmentioning

confidence: 99%

Section: B Compared With Other Methodsmentioning

confidence: 99%

Section: B Compared With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Chen et al [24] used the random forest ensemble learning algorithm for fault detection of PV array. In reference [25,26], the newly deep residual network model trained by the adaptive moment estimation deep learning algorithm is built for fault diagnosis of PV arrays. The intelligent classification method avoids the complex process of modelling and the classification process is easy to implement, but this method requires a large amount of fault sample data to train the model.…”

Section: Introductionmentioning

confidence: 99%

A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

Zhao¹,

Sun²,

Zhou³

et al. 2020

International Journal of Photoenergy

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There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.

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FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial‐of‐service attack detection in cloud using spark architecture

Ramkumar¹,

Ramasamy

Azees³

et al. 2022

Adaptive Control & Signal

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Cloud computing is an emerging standard in modern days for the purpose of sharing huge data, as it affords numerous user friendly behaviors. Cloud computing services offer an extensive range of resource pool in order to maintain huge scale data. Although, cloud computing model is disposed to several cyber-attacks and security problems regarding cloud structure, because of the dynamic and distribute character and exposures in virtualization implemen-

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Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions

Cited by 243 publications

References 63 publications

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

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

A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial‐of‐service attack detection in cloud using spark architecture

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