“…(1 is yes, 0 is no) 1 (2,3,4,6,7,8,9]; 0 [1,5,10] Set of acting CBs C9 (1,2); C21 (5,6); C7 (7,8); C6 (9,10)…”
Section: Table III Connectivity Analysis Resultsmentioning
confidence: 99%
“…Since Liacco and Kraynak analyzed power grid fault event processes using the tripping action information of PRs and CBs in 1969 [1], researchers have gradually paid more attention to the development of power grid fault analysis systems that can provide online decision support. Over the past four decades, with the development of computer technology and artificial intelligence, online power grid fault diagnosis has been applied in several power grids.…”
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.
“…(1 is yes, 0 is no) 1 (2,3,4,6,7,8,9]; 0 [1,5,10] Set of acting CBs C9 (1,2); C21 (5,6); C7 (7,8); C6 (9,10)…”
Section: Table III Connectivity Analysis Resultsmentioning
confidence: 99%
“…Since Liacco and Kraynak analyzed power grid fault event processes using the tripping action information of PRs and CBs in 1969 [1], researchers have gradually paid more attention to the development of power grid fault analysis systems that can provide online decision support. Over the past four decades, with the development of computer technology and artificial intelligence, online power grid fault diagnosis has been applied in several power grids.…”
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.
“…The use of a logic-based "Automatic System Trouble Analysis" system to diagnose faults in a power system was first presented in 1969 [2]. Rules using switchgear status, protection status and voltage indications defined the occurrence of system faults and maloperations.…”
This paper presents two expert system developments which are each concerned with utilizing, t o the best effect, the increasing volume of SCADA (Supervisory Control And Data Acquisition) system data available t o power system control staff. The systems presented, APEX and RESPONDD, are aimed at the two related fields of alarm processing, and fault diagnosis respectively. The areas of commonality between these systems are discussed as well as details specific t o each separate system, including a case study of practical operation of each.
“…The reasons are: diagnosis is well suited to knowledge-based approaches; it is an important operating function in energy control centers; it would benefit from automation [1], [8], [9]; and it largely remains to be automated. In the U.S., only the Cleveland Electric control center has an automatic trouble analysis function [8], [9]. The function was developed in the late 60's and early 70's in a pioneering effort.…”
This paper describes two knowledge-based programs. The first simulates the behavior of automatic protection schemes in power networks. The second is an expert system for the diagnosis of faults. Both are coded in OPS5--a widely available language for writing rule-based programs.The user and the programs communicate over a Blackboard which is a database for messages. The Blackboard has been organized so that the addition of new programs, whether knowledge-based or algorithmic, will be relatively easy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.