Neural Detection of Foreign Objects for Transmission Lines in Power Systems

Abstract

Ensuring the normal operation of the transmission lines, which provides a path for directing the transmission of energy from one place to another, is a prerequisite for delivering power to cities and enterprises. A major threat comes from foreign objects, which may cause interruption of power transmission. Compared with traditional manual method, which not only consumes a lot of manpower, but more importantly, affects the safety and efficiency of power network, in this paper, we apply a neural detection of foreign objects for transmission lines. Transfer learning and data augmentation are used to mitigate data shortages. Experimental results show that even with small training data, the neural detection with transfer learning and data augmentation is an effective method for this task without loss of real-time property.