2 results about "Fault detection and identification" patented technology

The application belongs to the technical field of smart grid fault detection and identification, and relates to a power distribution network intelligent inspection and fault diagnosis method based on multi-source information fusion. The method comprises an unmanned aerial vehicle inspection platform, an airborne edge computing terminal, a cloud operation and management platform, a PMS system and a mobile terminal. The application synchronously collects visible light images, infrared thermal images and independent temperature sensing data through the unmanned aerial vehicle platform, and utilizes deep learning and multi-modal fusion technology to realize data complementation and collaborative analysis at a feature level, so that real-time and accurate identification of power distribution network lines and equipment faults is finally completed.

This invention provides a method and system for detecting faults in distribution network lines based on unmanned aerial vehicle (UAV) inspection, belonging to the field of power grid fault detection and identification technology. The method includes: determining a target line based on line anomaly information and obtaining the path information of the target line; dispatching a UAV to inspect along the target line direction, obtaining magnetic field measurements, UAV position data, and the distance between the UAV and the target line, thus obtaining a magnetic field sampling sequence; constructing a magnetic field distribution map along the target line based on the preprocessed magnetic field sampling sequence; determining suspected anomaly locations, fault types, and fault location information based on the magnetic field distribution map, and outputting the magnetic field distribution map, fault type, and fault location information to a display terminal for display. This method enables rapid inspection of abnormal distribution network lines, achieving identification of suspected anomaly locations, fault type discrimination, fault location information determination, and visual display, thereby improving the efficiency of distribution network line fault inspection and maintenance.