Open AccessDeep‐learning based fault diagnosis using computer‐visualised power flow
Author(s) -
Wang Songyan,
Fan Shixiong,
Chen Jianwen,
Liu Xingwei,
Hao Bowen,
Yu Jilai
Publication year - 2018
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.2018.5254
Subject(s) - robustness (evolution) , computer science , convolutional neural network , network topology , electric power system , fault (geology) , artificial intelligence , deep learning , artificial neural network , topology (electrical circuits) , feature extraction , pattern recognition (psychology) , power (physics) , engineering , electrical engineering , physics , quantum mechanics , seismology , geology , biochemistry , chemistry , gene , operating system
Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep‐learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer‐visualised images. Using massive power‐flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature‐map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep‐learning based method may diagnose system faults effectively.