Classification of mode damping and amplitude in power systems using synchrophasor measurements and classification trees

This paper details the use of classification trees to predict mode damping using powerflow data from power system models. Power systems are complex with vast amounts of data being collected from measurements made via wide-area measurement (WAMS) and SCADA systems. Classification trees are introduced as a means to handle vast quantities of powerflow data which can be used to classify mode damping and amplitude. In reality, the classification trees would be trained on real power system data from SCADA and WAMS. A case study based on real field data from the Ecuadorian power system is presented where interconnectors to Colombia tripped in response to a poorly damped mode of oscillation. Testing of this methodology shows the feasibility of this approach with accurate classification of mode damping and amplitude on training and testing data sets. By highlighting significant variables affecting mode damping and amplitude, a decision tool for system operators can be developed that will suggest the optimal course of action to remedy any near instability or unstable electromechanical oscillations in transmission networks.