Open AccessData‐driven approach for real‐time distribution network reconfiguration
Author(s) -
Yin Ziyang,
Ji Xingquan,
Zhang Yumin,
Liu Qi,
Bai Xingzhen
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1733
Subject(s) - control reconfiguration , computer science , power flow , minimisation (clinical trials) , reliability (semiconductor) , metaheuristic , convolution (computer science) , data set , mathematical optimization , real time computing , electric power system , power (physics) , algorithm , artificial neural network , embedded system , artificial intelligence , mathematics , statistics , physics , quantum mechanics
Finding a global optimal solution to the distribution network reconfiguration (DNR) problem in a short time is a challenging task. This study proposes a real‐time online data‐driven DNR (3DNR) method. Power loss minimisation, lowest bus voltage maximisation and reliability maximisation are taken as objectives. First, in this study, a methodology combining heuristic algorithm and metaheuristic algorithm to solve DNR is proposed. Then a set of data that satisfies the data drive model requirements is obtained. Next, the improved convolution neural network is used to train the data set of DNR. Unlike the state‐of‐art methods, the proposed 3DNR can realise the real‐time online reconfiguration without power flow calculation. The feasibility and effectiveness of the proposed method are demonstrated on IEEE‐34, IEEE‐123, and a practical distribution system in Taiwan.