Image-Based State Recognition for Disconnect Switches in Electric Power Distribution Substations

Abstract

Knowing the state of the disconnect switches in a power distribution substation is important to avoid accidents, damaged equipment, and service interruptions. This information is usually provided by human operators, who can commit errors because of the cluttered environment, bad weather or lighting conditions, or lack of attention. In this paper, we introduce an approach for determining the state of each switch in a substation, based on images captured by regular pan-tilt-zoom surveillance cameras. The proposed approach includes noise reduction, image registration using phase correlation, and classification using a convolutional neural network and a support vector machine fed with gradient-based descriptors. By combining information given in an initial labeling stage with image processing techniques to reduce variations in viewpoint, our approach achieved 100% accuracy on experiments performed at a real substation over multiple days. We also show how modifications to the standard phase correlation image registration algorithm can make it more robust to lighting variations, and how SIFT (Scale-Invariant Feature Transform) descriptors can be made more robust in scenarios where the relevant objects may be brighter or darker than the background.