DocumentCode :
47943
Title :
A robust state estimator for medium voltage distribution networks
Author :
Jianzhong Wu ; Yan He ; Jenkins, Nick
Author_Institution :
Sch. of Eng., Cardiff Univ., Cardiff, UK
Volume :
28
Issue :
2
fYear :
2013
fDate :
May-13
Firstpage :
1008
Lastpage :
1016
Abstract :
A closed-loop robust distribution state estimator was investigated. An approach that is suitable for medium voltage distribution networks which are either under-determined with limited real-time measurements or over-determined but with delayed information from smart meters was developed. The state estimator was designed to be robust against the effect of measurement errors, the type, location and accuracy of measurements, as well as temporary failure of the smart metering communication system. A machine learning function provides reliable input information to a robust state estimation algorithm. The output of the state estimator is then fed back to the machine learning function creating a closed-loop information flow which improves the performance of the state estimator. Test results and analysis on a 33-node system are provided.
Keywords :
distribution networks; estimation theory; learning (artificial intelligence); measurement errors; smart meters; state estimation; closed loop robust distribution state estimator; delayed information; machine learning function; measurement errors; medium voltage distribution networks; smart metering communication system; Computer aided software engineering; Machine learning algorithms; Measurement uncertainty; Robustness; State estimation; Time series analysis; Distribution network; machine learning; robust statistics; smart metering; state estimation;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2012.2215927
Filename :
6313963
Link To Document :
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