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In order to expand the detection range and ensure the operation stability, the substation multi-source perception blind compensation detection method based on equipment visual blind area recognition and significance detection is studied. Acoustic sensors are used to collect acoustic signals from visual blind areas of equipment. The characteristics of noise signal are identified by wavelet analysis and noise reduction. The supercomplex Fourier transform model was used to extract the important region in the device image, and the texture features of the region were detected by Gabor filter. The blind compensation detection feature vector is formed by integrating two multi-source sensing features. The detection model of support vector machine is input to complete the blind compensation detection of the substation. The experimental results show that the proposed method is effective for the sound signal feature recognition in the visual blind area and the texture feature detection in the significant area of the device image. The different operating states of each equipment detected by the multi-source sensing feature vector are more accurate, which can realize the purpose of the multi-source sensing blind compensation check of the substation and ensure the safe and stable operation of the substation.

international journal of emerging electric power systems

Multi-source perceptual blind compensation inspection method for substation based on equipment’s visual blind area identification and saliency detection