Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images
AbstractAccurate and timely detection of insulator flashover on power transmission lines is of paramount importance to power utilities. Most available solutions mainly focus on the exploitation of the flashover mechanism or the discharge area detection, rather than the identification of a damaged area due to flashovers using captured aerial images. To this end, this paper proposes a multi-saliency aggregation-based porcelain insulator flashover fault detection approach. The target area of the insulator is determined using the Faster-Pixelwise Image Saliency by Aggregating (F-PISA) algorithm based on the color and structural features. The color model can be established based on the color feature of the damaged areas on the insulator surface, and hence the damaged area can be identified. Based on the information obtained above, the contour information can be extracted. With the preceding process, the fault location can be confirmed with a good accuracy. The performance of the proposed detection approach is assessed through a comparative study with other available solutions. The numerical result demonstrates that the suggested solution can detect the insulator flashover with improved performance in terms of the average detection rate and average efficient detection rate. Additional analysis is carried out to evaluate its robustness and real-time performance, which confirms its deployment feasibility in practice. View Full-Text
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Zhai, Y.; Cheng, H.; Chen, R.; Yang, Q.; Li, X. Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images. Energies 2018, 11, 340.
Zhai Y, Cheng H, Chen R, Yang Q, Li X. Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images. Energies. 2018; 11(2):340.Chicago/Turabian Style
Zhai, Yongjie; Cheng, Haiyan; Chen, Rui; Yang, Qiang; Li, Xiaoxia. 2018. "Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images." Energies 11, no. 2: 340.
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