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Aiming at the problem of multi-modal fault detection of different equipment in ultrahigh voltage (UHV) substations, a method for based on robot inspection and deep learning is proposed. First, the inspection robot is used to collect the image data of different devices in the station and the source data is preprocessed by standard image augmentation and image aliasing augmentation. Then, the HSV color space model based on saliency area detection is used to extract equipment defect areas, which improves the accuracy of defect image classification. Finally, the traditional YOLOv3 network is improved by combining the residual network and the K-means clustering algorithm, and the detailed flow of the corresponding detection method is proposed. The proposed detection method and the other three methods were compared and analyzed under the same conditions through simulation experiments. The results show that the detection accuracy and recall rate of the method proposed in this study are the largest, which are 95.9% and 91.3%, respectively. The average detection accuracy under multiple intersection ratio thresholds is also the highest, and the performance is better than the other three comparison algorithms.

A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning