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.
Energy absorption performance is one of the most important indexes in the vehicle safety during impact. Research on the car frontal structure energy performance and structure optimization was conducted in this paper. Whole vehicle model was established by HyperMesh and simulated in LS-DYNA. Simulation results indicated that modification was needed for the original structure to meet the industry requirements. Based on simplified whole vehicle model, orthogonal design optimization was implemented, including bumper cross beam material (A), bumper cross beam thickness (B), energy absorber groove distance (C), and front longitudinal beam groove number (D), with 3 levels for each factor. The best option was B3D1A3C3 which was gained by using range analysis and integrated balance method. Simulation results showed that both front and total energy absorptions were improved. The optimized structure increased front energy absorption to 51.1%, which can meet the industry requirement.
Colored spun fabrics are difficult to accurately characterize with a local binary pattern due to texture anisotropy caused by the uneven distribution of dyed fibers. In this paper, we present a texture representation model based on spatial and frequency characteristics. The proposed model takes advantage of the local binary pattern and local phase quantization to extract the texture of woven fabric. Then, the two features are connected in series, and the features of dimension reduction by principal component analysis are used to represent the texture of the fabric image. Finally, the hierarchical hybrid classifier is applied to classify the fabric structure. The experimental results show that the local phase quantization feature is robust to the fuzzy transformation and the texture representation model has a stronger ability of texture description than the single local binary pattern feature, with the average classification accuracy of 97.59% on 336 samples. In addition, compared with the deep learning algorithm, the texture representation algorithm can ensure a high classification accuracy.
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