Open AccessTopology identification in distribution networks based on alternating optimisation
Author(s) -
Feng Renhai,
Yuan Wanqi,
Xiao Meng,
Zhao Zheng,
Wang Qiulin
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.2020.0600
Subject(s) - overfitting , mathematical optimization , computer science , regular polygon , gaussian , norm (philosophy) , mathematics , topology optimization , white noise , network topology , algorithm , topology (electrical circuits) , artificial neural network , artificial intelligence , engineering , physics , geometry , structural engineering , quantum mechanics , combinatorics , finite element method , political science , law , telecommunications , operating system
Topology identification (TI) is an essential problem in the distribution network due to exponentially growing power grid size in recent years. In this study, it is reformulated as a regularised alternating convex optimisation problem. Then an application based on the current injection model is proposed. Compared to the traditional algorithm optimising l 1 ‐norm, which may lead to overfitting, a new l 2 ‐norm minimisation problem with l 1 ‐norm regularisation is proposed to solve the trade‐off problem with non‐convex constraints. The proposed method reduces the size of the training data set compared with the traditional TI method. Simulation results show that the recovery performance of the proposed algorithm is superior to the traditional one in additive white Gaussian noise scenario.