Accurate fault area localization is a challenging problem in resonant grounding systems (RGSs). Accordingly, this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs. Firstly, a faulty feeder identification algorithm based on a Bayesian classifier is proposed. Three characteristic parameters of the RGS (the energy ratio, impedance factor, and energy spectrum entropy) are calculated based on the zero-sequence current (ZSC) of each feeder using wavelet packet transformations. Then, the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode. With this result, the faulty feeder can be finally identified. To find the exact fault area on the faulty feeder, a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units (FTUs). The FTUs can provide the information on the ZSC at their locations. Through wavelet-packet transformation, ZSC dominant frequency-band waveforms can be obtained at all FTU points. Similarities of the waveforms of characteristics at all FTU points are calculated and compared. The neighboring FTU points with the maximum diversity are the faulty sections finally determined. The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods. Finally, the effectiveness of the proposed method is validated by comparing simulation and experimental results.
As the traditional methods of insulator fault detection rely on the low-level feature extraction of images and classifier design, it is difficult to achieve fault detection of insulator for images with complex background. To address this issue, a fault detection method using second-order full convolutional network (SOFCN) is proposed in this paper. Firstly, the first-order FCN is used to learn the image features to segment insulator areas from images with complex background. Secondly, the mathematical morphology reconstruction operation is used to improve the segmentation result to get the accurate localization of the insulator areas. Finally, the FCN network is used again to detect the insulator fault and obtain the fault region. Experiments show that the proposed SOFCN is not only able to obtain accurate insulator region, but also able to effectively suppress the interference of noninsulator region. Compared to the conventional methods, the proposed SOFCN obtains higher recognition accuracy without feature extraction and the selection of a classifier. Moreover, the computational complexity of the proposed method is low. Furthermore, compared to the classical CNN and FCN segmentation methods, the proposed SOFCN can effectively suppress complex background interference to improve the accuracy of insulator fault detection.
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