Estimating power system electromechanical modes and mode shapes using modern system identification techniques.

摘要

Electric power systems exhibit low frequency oscillations associated with dynamics known as electromechanical modes. A mode is described by the frequency, damping, and shape of the oscillation. The mode shape defines the amplitude and phasing of the oscillation throughout the system. Knowledge of the electromechanical modal properties of a power system is of great importance to its safe and reliable operation. If the damping of a particular mode is allowed to become too low, the oscillation of the mode may grow out of control and cause a system wide outage like the one observed by the Western Electricity Coordinating Council in 1996. Therefore, accurate estimates of the electromechanical modes are required.;In the past, the modes were estimated through the creation and maintenance of detailed models. When linearized, an eigenanalysis of the state matrix associated with the system provides the complete modal information. The accuracy of the estimated modes, however, is dependent on the accuracy of the model, which for the 1996 outage proved to be inadequate for the conditions that led to the event. In the years since, several mode estimation schemes based on measured power system data have been developed using modern system identification techniques. These methods benefit from the fact that a detailed system model is not required and they can also serve to validate and, if necessary, update the detailed system models.;This dissertation presents two new methods for mode estimation from measured data. The first uses transfer functions constructed between pairs of system outputs to estimate the mode shape. The second examines the elements of the multichannel system transfer function to estimate the modal frequency, damping, and shape. Both methods benefit from the fact that they may be implemented using any one of a number of available system identification techniques. Typically the accuracy of the mode estimates is assessed using bootstrapping. Here a more efficient method of bootstrapping based on a derived asymptotic parameter distribution is presented. Each of the new methods' performance is verified using both simulated and measured data.