Intelligent Identification of Transmission Line Defects Based on Fast Neural Network Model
| Autor: | Yishi Yue, Yanhui Zou, Xianyong Xu, Ting Li, Yaoheng Xie, Junxingxu Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science Real-time computing 02 engineering and technology 010501 environmental sciences Grid Fault (power engineering) 01 natural sciences Visualization Identification (information) Electric power transmission Transmission line 0202 electrical engineering electronic engineering information engineering Overhead (computing) 020201 artificial intelligence & image processing 0105 earth and related environmental sciences |
| Zdroj: | 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). |
| DOI: | 10.1109/ei247390.2019.9061904 |
| Popis: | The hidden risk management and governance of the important crossing sections of overhead transmission lines is one of the key tasks of State Grid Corporation in recent years. With the wide application of new technologies such as unmanned aerial vehicles and visual online monitoring systems, massive inspection image data has been obtained. Most of the image data is identified and diagnosed manually at present, which is easy to cause misjudgment, and the recognition efficiency is extremely low. In this paper, the multi-source monitoring data of overhead transmission lines are collected and organized. Based on the fast neural network model, an intelligent fault identification method for transmission line visualization data is proposed for improving the utilization efficiency of transmission line monitoring data and helping to fully understand the running status of the equipment. |
| Databáze: | OpenAIRE |
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