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An analytical method for mapping alarm information to model of power grid fault diagnosis
Author(s) -
Zhang Xu,
Wei Juan,
Yue Shuai,
Zha Xiaobing
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22635
Subject(s) - fault (geology) , alarm , computer science , fault model , fault indicator , reliability engineering , data mining , grid , key (lock) , matching (statistics) , real time computing , stuck at fault , power (physics) , medical diagnosis , fault detection and isolation , artificial intelligence , engineering , electrical engineering , electronic circuit , computer security , statistics , actuator , physics , geometry , mathematics , quantum mechanics , seismology , geology , medicine , pathology
Online fault diagnosis systems have recently been applied in power grids. However, the complex modeling and high‐quality requirements for power grid fault diagnosis have restricted the wide application of online systems. This paper proposes an automatic method for mapping alarm information to a fault diagnosis model. First, we automatically extract the basic logic variables from the alarm information. These can be used in power grid fault diagnoses by employing a key character‐matching algorithm. We then build the associative relationship between electrical devices and circuit breakers based on connection analysis. Finally, an associative matrix of all electrical devices is designed to express the cooperative relationship between different devices in a fault event based on the short‐circuit power mark. Based on these modules, cause–effect events expressing the associated relationship between alarms are established according to the protection configuration principle and protective relay setting principle. Expressing the associated relationship between alarms according to the logical requirements of a fault diagnosis model enables the automatic mapping from alarm information to fault diagnosis model to be realized. The validity of the proposed method for online fault diagnosis is verified using a real fault case that occurred in a power grid.