The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.
Aiming at the fast opening and closing speed of the GIS isolation/grounding switch, manual observation is more difficult, so it is difficult to judge the current switch status. This paper proposes an OpenCV-based image identification algorithm to identify the position of the switch movable contact during the opening and closing process of the isolating switch, thereby judging the state of the isolating switch. This system uses Raspberry Pi as the main hardware core, the server drives the CMOS camera through Raspberry Pi 4B, collects image information in the GIS optical observation window, and performs simple processing, and transmits it to the Raspberry Pi 4B based on the UDP protocol as the main core. In the upper computer and adopt the target detection algorithm based on OpenCV to track the current isolation/grounding switch contact position and determine the current opening and closing state.
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