Open AccessFault diagnosis and identification of malfunctioning protection devices in a power system via time series similarity matching
Author(s) -
Xu Bing,
Wang Chongyu,
Wen Fushuan,
Palu Ivo,
Pang Kaiyuan
Publication year - 2020
Publication title -
energy conversion and economics
Language(s) - English
Resource type - Journals
ISSN - 2634-1581
DOI - 10.1049/enc2.12008
Subject(s) - correctness , fault (geology) , alarm , computer science , fault model , set (abstract data type) , similarity (geometry) , real time computing , electric power system , matching (statistics) , reliability engineering , data mining , process (computing) , stuck at fault , series (stratigraphy) , power (physics) , fault detection and isolation , artificial intelligence , engineering , algorithm , mathematics , image (mathematics) , quantum mechanics , electronic circuit , programming language , electrical engineering , biology , paleontology , statistics , actuator , aerospace engineering , operating system , physics , seismology , geology
Alarm messages uploaded to a dispatch centre following the failure of a power system contain extensive temporal information. The accuracy and speed of fault diagnosis can be improved upon taking full advantage of such temporal information contained in these messages. From this standpoint, a power system fault diagnosis model based on time series similarity matching is proposed herein. First, a set of suspected faulty components can be determined after the occurrence of a fault or a set of faults. Then, on the basis of the specifications of protection devices, including protective relays and circuit breakers, and the defined time series model, a set of alarm hypothesis time series is generated, containing action process information of the protection devices involved in these suspicious components. Meanwhile, the alarm messages received by the dispatch centre are pre‐processed, and the alarm information time series are obtained. Subsequently, the fault component is obtained by calculating the similarity between each series in the alarm hypothesis time series set and the alarm information time series. Finally, the correctness and effectiveness of the proposed fault diagnosis model are demonstrated via a real‐life scenario in a local power system in China.