| Summary: | Aiming at the problems of low efficiency, poor accuracy and untimely defect detection of traditional power grid equipment inspection methods, this paper proposed a power grid intelligent inspection management system based on physical ID. And a method for identifying high-risk alarm areas and fault location of transmission channel is proposed. By using big data mining and unsupervised clustering machine learning algorithm, the problems of poor accuracy and slow calculation speed of a large number of alarm data area division are fundamentally solved, and the functions of dynamic alarm and location affected by external force destruction, foreign object intrusion and environment are realized. The results show that compared with the traditional methods, the proposed method has higher efficiency and accuracy, and lower fault trip rate.
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