Measurement based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network
Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications. Wide area measurement systems (WAMS) based on synchrophasors make power system dynamics visible to system operators, delivering an accurate picture of overall operating conditions. However, in actual field implementations, some measurements can be inaccessible for various reasons, e.g. most notably communication failure. To reconstruct these inaccessible measurements, in this paper, the radial basis function artificial neural network (RBF ANN) is used to estimate the system dynamics. In order to find the best input features of the RBF ANN model, geometric template matching (GeTeM) and quality threshold (QT) clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system. The proposed method is tested and verified on the Eastern Interconnection (EI) transmission system in the United States. The results obtained indicate that the proposed approach provides a compact and efficient RBF ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.