69cf3305_bstracts___IEEE_GreenTech_2013__Category____Sub_Category
[Paper Title, Abstract] Bad data detection and identification using neural network-based reduced model state estimator Show AbstractHide Abstract This paper explores the capability of the reduced model artificial neural networks (ANNs)-based power system state estimator to accurately identify single and multiple bad data. This state estimator uses fewer measurements than conventional state estimators and does not require network observability analysis. A comparison of the single bad data detection and identification between the proposed state estimator and the Weighted Least Squares (WLS) state estimator on GE 6-bus and IEEE 14-bus power systems is provided. The results show that the proposed state estimator is more accurate than the WLS state estimator.Furthermore, the proposed methodology is a great alternative to the conventional techniques and is therefore well suited for smart grid applications. [Schedule] TTM-GWRA [Category :: Sub-Category] 
Green IT and Communications
Abstracts « IEEE GreenTech 2013
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