Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network-Reference-Cited by-同舟云学术

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Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network

Published:2021-06 Issue: Volume:195 Page:107132
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Ji Xingquan,Yin Ziyang,Zhang Yumin,Xu Bo,liu Qi

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference45 articles.

1. Pareto dominance-based multiobjective optimization method for distribution network reconfiguration;Asrari;IEEE Trans. Smart Grid,2016

2. A fast heuristic network reconfiguration algorithm to minimize loss and improve voltage profile for a smart power distribution system;Raut,2017

3. Feeder reconfiguration in distribution networks based on convex relaxation of OPF;Peng;IEEE Trans. Power Syst.,2015

4. Two circular-updating hybrid heuristic methods for minimum-loss reconfiguration of electrical distribution network;Mohd Zin;IEEE Trans. Power Syst.,2013

5. A fast nondominated sorting guided genetic algorithm for multi-objective power distribution system reconfiguration problem;Eldurssi;IEEE Trans. Power Syst.,2015

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A new continuous variables and convex relaxation based formulation for reconfiguration of unbalanced mutually coupled active distribution networks;Electric Power Systems Research;2024-03

2. A Review of Intelligent Verification System for Distribution Automation Terminal based on Artificial Intelligence Algorithms;Journal of Cloud Computing;2023-10-16

3. Dynamic reconfiguration of three‐phase imbalanced distribution networks considering soft open points;Energy Conversion and Economics;2023-10

4. Distribution system reconfiguration based on MILP considering voltage stability;Electric Power Systems Research;2023-09

5. Adaptive real-time reconfiguration gate scheduling scheme using time perceptive stream;Automatika;2023-08-15

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