Analysis Of Interarea Electromechanical Oscillations In The Power Systems Based On Wams Data

Interarea electromechanically oscillations are inherent to interconnected power systems. Oscillation modes with low damping ratios may cause sustained oscillations, which will set limits to the power transferring capability of the tie lines and go against the economic operation of the power systems; while oscillation modes with negative damping ratios may cause oscillations with growing amplitudes, which may pose threat to the stability of the power system; those growing oscillations, if not observed in time or controlled properly, may even cause catastrophic blackout events. Considering the potential serious threat of the low frequency oscillations to the power systems, both the IEEE and the CIGRE have special publications focusing on this subject.The traditional way to study the interarea oscillations is to linearize the detailed state space model of the power system around an operating point and conduct eigen-analysis of the system state matrix. The model based methods are constrained by the following facts:for large interconnected power systems, it is not an easy job to obtain its detailed model accurately; even worse, the operation condition of the system is always changing, and if the model of the system is not updated in time, it cannot reflect the oscillation characteristics of the system correctly. The growing installation of phasor measurement units (PMUs) in the power systems makes it possible to analyze the interarea oscillations based on wide area measurements. Since the PMU measurements correctly reflect the current operation condition of the power systems, methods based on measurements can effectively complement that based on detailed system model. The main job of this dissertation is to analyze the interarea electromechanical oscillations based on wide area measurements.The ITD (Ibrahim Time Domain) method, which was formerly applied to analyze the vibration characteristics of the architecture or mechanism structures, was introduced to estimate the interarea modes in the power systems using free response signals. The performance of the ITD method was verified and evaluated in the 16-machine power system model, which is the reduced order model of the New York/New England power system. By passing the input signals through a zero-phase low-pass filter, the robustness of the ITD method to measurement noise can be effectively improved. The ITD method is based on multi-channel input signals, and has the ability to estimate the frequencies, damping ratios as well as the mode shapes simultaneously, and these three parameters can fully describe the characteristics of a specific mode; while methods based on single-channel input signal (such as the Prony method, the TLS-ESPERIT method, the wavelet method, and the Kalman filtering technique) can only estimate the frequencies and damping ratios. Comparison results between the ITD method and the SSI (Stochastic Subspace Identification) method show that both methods are able to estimate the interarea modes accurately, however, the ITD method consumes much less cup time than the SSI method does, because the SSI method has to perform singular value decomposition of a large matrix, which is time consuming. In the situation when only limited PMU measurements are available, as long as these available measurements have good observability for the interested modes, the ITD method can still give acceptable estimation results for these modes. The ITD method was verified using PMU data obtained from a real power grid in Southwest West China, and identified the 0.32 Hz oscillation mode between this power grid and another larger power grid.Theoretically, the ITD method is based on free responses, and its application relies on the occurrence of major disturbances in the power systems. However, for most of the time the system is operating under ambient conditions, during which the responses of the system are caused by random fluctuations of the loads, which are called random responses. Under certain assumptions, the the random decrement technique (RDT) and the natural excitation technique (NExT) are able to extract the ring-down signatures from the random responses. By combining these two techniques with the ITD method, the RDT-ITD method and the NExT-ITD method are employed to estimate the interarea modes during ambient operation conditions. Simulation results in the 16-machine power system model show that, compared with the RDT-Prony method proposed in the former literature, the RDT-ITD method and the NExT-ITD method are much more robust to measurement noise. Estimation results using real PMU measurements obtained from a real power grid in Southwest West China show that the RDT-ITD method and the NExT-ITD method are able to identify the 0.75-0.8 Hz mode existed inside this power grid.The NExT-ERA method was formerly used in the literature to estimate the interarea modes during ambient operation of the power systems, however, the former literature only focused on the estimation of the frequencies and damping ratios. In this dissertation, the ability of the NExT-ERA method in estimating the mode shapes were verified and evaluated. Meanwhile, the RDT-ERA method was proposed to estimate the interarea modes. The performance of RDT-ERA、NExT-ERA、RDT-ITD and NExT-ITD were compared in a common simulation model, which is the 16-machine power system model. Estimation results using real PMU measurements obtained from a real power grid in Southwest West China show that both the RDT-ERA method and the NExT-ERA method are able to identify the 0.75-0.8 Hz mode existed inside this power grid.Both the ambient excitations and the measurement noise are stochastic in nature, and may bring uncertainties to the estimation results during ambient operation conditions. The estimation result from one time of experiment is not convictive. Therefore, in this dissertation, the effects of these two kinds of stochastic factors are analyzed in a statistical way by conducting Monte Carlo simulations. The Monte Carlo simulations are designed in such a way that it can distinguish whether the uncertainties in the estimation results are caused by the ambient excitations or by the measurement noise. So, when studying the effect of the ambient excitations, the measurement noise is fixed for every time of experiment, and vice versa.