DocumentCode :
1279978
Title :
Data visualisation and identification of anomalies in power system state estimation using artificial neural networks
Author :
Souza, J.C.S. ; Da Silva, A. M Leite ; Da Silva, A. P Alves
Author_Institution :
Dept. of Electr. Eng., Fluminense Federal Univ., Brazil
Volume :
144
Issue :
5
fYear :
1997
fDate :
9/1/1997 12:00:00 AM
Firstpage :
445
Lastpage :
455
Abstract :
Bad data identification is one of the most important and complex problems to be addressed during power system state estimation, particularly when both analogical and topological errors (branch or bus misconfigurations) are to be considered. The paper proposes a new method that is capable of distinguishing between analogical and topological errors, and also of identifying which are the bad measurements or the misconfigured elements due to unreported or incorrectly reported line outages, bus splits etc. The method explores the discrimination capability of the normalised innovations (the differences between the latest acquired measurements and their corresponding predicted quantities), which are used as input variables to an artificial neural network that provides, in the output, the anomaly identification. Data projection techniques are also used to visualise and confirm the discrimination capability of the normalised innovations. The method is tested using the IEEE 24-bus test system, where several types of errors have been simulated, including single and multiple bad measurements, topology errors involving branches or buses etc
Keywords :
data visualisation; error analysis; power system analysis computing; power system state estimation; self-organising feature maps; IEEE 24-bus test system; Kohonen self-organising map; analogical errors; anomalies identification; artificial neural networks; bad data identification; bus splits; data projection techniques; data visualisation; discrimination capability; errors simulation; feature selection; forecasting-aided state estimation; incorrectly reported line outages; input variables; misconfigured elements; multiple bad measurements; normalised innovations; power system monitoring; power system state estimation; single bad measurements; topological errors; topology errors; unreported line outages;
fLanguage :
English
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
Publisher :
iet
ISSN :
1350-2360
Type :
jour
DOI :
10.1049/ip-gtd:19971168
Filename :
629503
Link To Document :
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