Data‐driven approach for real‐time distribution network reconfiguration

Autor:	Ziyang Yin, Xingquan Ji, Yumin Zhang, Qi Liu, Xingzhen Bai
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	metaheuristic algorithm data drive model requirements improved convolution neural network 3DNR real‐time online reconfiguration practical distribution system Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Zdroj:	IET Generation, Transmission & Distribution, Vol 14, Iss 13, Pp 2450-2463 (2020)
Druh dokumentu:	article
ISSN:	1751-8695 1751-8687
DOI:	10.1049/iet-gtd.2019.1733
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/80995e5c32274063be803b220681e782 Zobrazit plný text záznamu Plný text View record in DOAJ