Research and Application of Artificial Technology for Substation Environment Surveillance System

In order to satisfy the requirements of power operation scene safety monitoring and lessen the human accidents, a novel framework of video surveillance in the substation based on artificial technology was presented. Firstly, a multi-class detector based on region-based fully convolutional networks (R-FCN) was proposed to realize the detection and localization of multiple targets. Then types of violation and defect were discriminated by secondary analysis for the workers or equipments based on detection results. In order to implement auto-alarm of flames and smoke, an algorithm for automatic smoke and flame detection is presented, using different methods to extract candidate regions and a unified CNN to recognition. In addition, a virtual electronic fence was designed to prevent workers from entering dangerous areas and other no-working areas. The experimental results demonstrate that the approach is effective for detecting illegal operations of works automatically and substation security monitoring, and it will improve the automation and intelligentization level of unmanned substation.