Open AccessTopology detection in power distribution system using kernel‐node‐map deep networks
Author(s) -
Xiao Mengmeng,
Wang Shaorong,
Ullah Zia,
Li Yan,
Arghandeh Reza
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.2020.0048
Subject(s) - topology (electrical circuits) , robustness (evolution) , kernel (algebra) , computer science , network topology , convolutional neural network , node (physics) , deep learning , phasor , artificial intelligence , electric power system , power (physics) , engineering , mathematics , computer network , structural engineering , combinatorics , biochemistry , chemistry , physics , quantum mechanics , electrical engineering , gene
This study is focused on real‐time topology detection (TD) problems in the power distribution system. The advent of distribution phasor measurement units offers additional opportunities to use deep learning methods for accurate TD of the distribution system. In this study, a new concept named the kernel‐node‐map is presented, and then a novel topology detection method, Kernel‐Node‐Map Deep Network (KNDN), for distribution system is developed. KNDN is based on a deep convolutional neural network and the kernel‐node‐map concept. This neural network is adaptive to the physical topology of the distribution system in the structure. The principle, training process, and application of KNDN are elaborated in detail. Testing results on the modified IEEE 33‐bus and 123‐bus distribution systems verify the effectiveness and robustness of the proposed KNDN methodology, and show its advantages compared with state‐of‐the‐art methods.