The effect of quality and availability of measurement signals on accuracy of on-line prediction of transient stability using decision tree method

This paper investigates the practical issues associated with on-line prediction of transient stability using decision tree (DT) method. The issues of quality and availability of the measurement signals provided by wide area measurement system (WAMS) are discussed and their effects on the accuracy of performance of the DT are evaluated. The surrogate split method included in the classification and regression tree (CART) algorithm is used to handle the unavailability of measurement signals, and noise present in the on-line data is modeled as white Gaussian noise (WGN) with various signal-to-noise ratio (SNR). Case studies are carried out in the 16 machine, 68 bus power system. The results quantify the reduction of the performance of the DT method.