TY - GEN
T1 - Online bad data outlier detection in PMU measurements using PCA feature-driven ANN classifier
AU - Mahapatra, Kaveri
AU - Chaudhuri, Nilanjan Ray
AU - Kavasseri, Rajesh
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/29
Y1 - 2018/1/29
N2 - This paper presents a method for online bad data outlier detection in PMU measurements. High resemblance of bad data in the form of outliers with that of system disturbances can cause control centers to take false decisions. The proposed method utilizes artificial neural network (ANN) in association with principal component analysis (PCA)-based feature extraction technique in order to classify bad data outliers and outliers caused by disturbances like faults. The extracted features using PCA from the reduced dimensional representation of the data is given as input to ANN. Bayesian regularization back-propagation-based learning algorithm is used for training ANN. The test results demonstrate online classification/detection of bad data of different amplitudes injected in measurements, both before and after disturbance, in two example power systems.
AB - This paper presents a method for online bad data outlier detection in PMU measurements. High resemblance of bad data in the form of outliers with that of system disturbances can cause control centers to take false decisions. The proposed method utilizes artificial neural network (ANN) in association with principal component analysis (PCA)-based feature extraction technique in order to classify bad data outliers and outliers caused by disturbances like faults. The extracted features using PCA from the reduced dimensional representation of the data is given as input to ANN. Bayesian regularization back-propagation-based learning algorithm is used for training ANN. The test results demonstrate online classification/detection of bad data of different amplitudes injected in measurements, both before and after disturbance, in two example power systems.
UR - http://www.scopus.com/inward/record.url?scp=85046372435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046372435&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2017.8273997
DO - 10.1109/PESGM.2017.8273997
M3 - Conference contribution
AN - SCOPUS:85046372435
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -