TY - GEN
T1 - Robust state-estimation procedure using a least trimmed squares pre-processor
AU - Weng, Yang
AU - Negi, Rohit
AU - Liu, Qixing
AU - Ilić, Marija D.
PY - 2011
Y1 - 2011
N2 - Based on real-time measurements, Static State Estimation serves as the foundation for monitoring and controlling the power grid. The popular weighted least squares with largest normalized residual removed, gives satisfactory performance when dealing with single or multiple uncorrelated bad data. However, when the bad data are correlated or bounded, this estimator has poor performance in detecting bad data, which leads to erroneous deleting of normal measurements. Similar to the Least Trimmed Squares(LTS) method of robust statistics, this paper considers a state estimator built on random sampling. However, different from previous robust estimators, which stop after estimation, we regard the LTS estimator as a pre-processor to detect bad data. A subsequent post-processor is employed to eliminate bad data and re-estimate the state. The new method has been tested on the IEEE standard power networks with random bad data insertions, showing improved performance over other proposed estimators.
AB - Based on real-time measurements, Static State Estimation serves as the foundation for monitoring and controlling the power grid. The popular weighted least squares with largest normalized residual removed, gives satisfactory performance when dealing with single or multiple uncorrelated bad data. However, when the bad data are correlated or bounded, this estimator has poor performance in detecting bad data, which leads to erroneous deleting of normal measurements. Similar to the Least Trimmed Squares(LTS) method of robust statistics, this paper considers a state estimator built on random sampling. However, different from previous robust estimators, which stop after estimation, we regard the LTS estimator as a pre-processor to detect bad data. A subsequent post-processor is employed to eliminate bad data and re-estimate the state. The new method has been tested on the IEEE standard power networks with random bad data insertions, showing improved performance over other proposed estimators.
UR - http://www.scopus.com/inward/record.url?scp=79958004258&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958004258&partnerID=8YFLogxK
U2 - 10.1109/ISGT.2011.5759135
DO - 10.1109/ISGT.2011.5759135
M3 - Conference contribution
AN - SCOPUS:79958004258
SN - 9781612842189
T3 - IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe
BT - 2011 IEEE PES Innovative Smart Grid Technologies, ISGT 2011
T2 - 2011 IEEE PES Innovative Smart Grid Technologies, ISGT 2011
Y2 - 17 January 2011 through 19 January 2011
ER -