DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems

Lu, Shibo; Sirojan, Tharmakulasingam; Phung, B.T.; Zhang, Daming; Ambikairajah, Eliathamby

doi:10.1109/access.2019.2909267

Cited by 109 publications

(53 citation statements)

References 23 publications

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“…Max pooling layer only keeps the maximum value within the specific square areas of the max-pooling kernel size, which is often employed after the CNN layer to reduce the output dimension. Therefore, the key information of the extracted features by the CNN layer can be preserved with reduced computation [21,22]. A leaky rectified linear unit (Leaky ReLu), as shown in Equation 2, is used as the activation function to alleviate the issue of vanishing gradient [22].…”

Section: Preliminary Theory Of Cnnmentioning

confidence: 99%

“…The procedure of the TDV layer is illustrated in Figure 4, every half-cycle current signal that has 10,000 data points is regarded as a measurement object. Firstly, these data points will be preprocessed using Min-Max normalization [21] and further arranged into a matrix with a size of 100 × 100 according to the sequence of the temporal domain, as shown in Equation 4, where x is the normalized input, while x raw is the original collected raw data. Secondly, this matrix is transposed and converted to a gray image with a value range from 0 to 255 for visualization.…”

Section: Tdv Layermentioning

confidence: 99%

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A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network

Yang

Chu

Zhang

et al. 2019

Sensors

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AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads’ work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.

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Section: Preliminary Theory Of Cnnmentioning

confidence: 99%

Section: Tdv Layermentioning

confidence: 99%

A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network

Yang

Chu

Zhang

et al. 2019

Sensors

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“…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%

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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“…They extracted the features of arc fault condition compared with the normal operation in the frequency domain. In [20]- [22], the discrete wavelet transform (DWT) and wavelet packet transform (DPT) ware also introduced to detect the arc fault condition in the frequency domain. This method decomposes the frequency response to improve the resolution under the desired frequency range, which can magnify the noise signal caused by the arc fault condition.…”

Section: Introductionmentioning

confidence: 99%

“…However, the arc fault detection performance using the frequency domain analysis depends on the frequency bandwidth selection. The conventional detection range is several tens kilo-hertz, which can degrade the detection accuracy by the switching noise of the inverter [18]- [22]. In [23]- [25], the artificial neural network and support vector machines were introduced to detect the arc fault condition.…”

Section: Introductionmentioning

confidence: 99%

DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

Park

Chae

2020

IEEE Access

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Arc fault detection is important technology to guarantee the safety of power systems and is therefore essential for producing practical power systems for real-world applications. However, fuses and arc fault detection devices (AFDD) struggle to detect series arc faults in DC systems, because the series arc fault induces small current variation between the normal and abnormal conditions. In addition, switching noise from the grid-connected inverter makes detecting arc fault conditions even more difficult. This paper proposes arc fault detection algorithm based on the relative comparison of current variability in terms of frequency spectrum and time series. The operational principle of the proposed algorithm is analyzed to detect the arc fault condition. In addition, the investigation of arc fault impedance using the small-signal modeling can obtain the resonant frequency of arc fault condition at low frequency range. From the impedance model, the frequency analysis range can be designed to avoid the switching noise of inverter. The performance of proposed arc fault detection algorithm is verified with a 3.8 kW grid-connected PV system and arc fault generator.

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DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems

Cited by 109 publications

References 23 publications

A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network

A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network

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

DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

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