Visible to the public Research on Self-Healing Technology for Faults of Intelligent Distribution Network Communication System

TitleResearch on Self-Healing Technology for Faults of Intelligent Distribution Network Communication System
Publication TypeConference Paper
Year of Publication2019
AuthorsLiu, Xiaobao, Wu, Qinfang, Sun, Jinhua, Xu, Xia, Wen, Yifan
Conference Name2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
KeywordsBusiness, Circuit faults, Communication networks, composability, computerised monitoring, Data analysis, Data Transmission, distribution network data monitoring, distribution network self-healing control, intelligent decision, intelligent distribution network communication system faults, intelligent power communication network, maintenance engineering, Monitoring, optimal control, power distribution control, power distribution faults, power engineering computing, power grids, power system, power system security, pubcrawl, real-time monitoring, resilience, Resiliency, self-healing control, self-healing networks, self-healing technology, SHC, Smart grids, smart power service
AbstractThe intelligent power communication network is closely connected with the power system, and carries the data transmission and intelligent decision in a series of key services in the power system, which is an important guarantee for the smart power service. The self-healing control (SHC) of the distribution network monitors the data of each device and node in the distribution network in real time, simulates and analyzes the data, and predicts the hidden dangers in the normal operation of the distribution network. Control, control strategies such as correcting recovery and troubleshooting when abnormal or fault conditions occur, reducing human intervention, enabling the distribution network to change from abnormal operating state to normal operating state in time, preventing event expansion and reducing the impact of faults on the grid and users.
DOI10.1109/ITNEC.2019.8729377
Citation Keyliu_research_2019