Stochastic subspace methods for large power system oscillation monitoring

Accurate knowledge and estimation of the low-frequency electromechanical oscillations are of vital importance for operational reliability of any power system. Such mode estimation basically can be done with two different approaches: using a power system model and by linearizing the equations about operating an equilibrium point or by using measurement based mode estimation methods. Measurement based algorithms for estimating low-frequency electromechanical modes serve as useful practical methods to monitor the modal properties of power system oscillations in real-time.;There are two different types of measurement data: ring-down data and ambient data. A ring-down data is from system response to a sudden disturbance in power system. For ambient data, power system is assumed to be operating at its quasi-steady-state condition while the system input is from continuous small random fluctuations in loads and other related small variations which are assumed to be white noise. Stochastic subspace methods are effective algorithms for modal estimation of a system from ambient data. They have a relatively simple order selection technique and they are good choices for handling large data and system dynamic changes. They also are effective methods in estimating mode shapes. The main weakness of the subspace method is that of high computational burden because it requires Singular Value Decomposition (SVD) of a large-dimensional matrix that makes it difficult to implement in real-time applications.;Besides these natural electromechanical modes that are excited by load variations, forced oscillations can be introduced into power systems from external mechanisms such as cyclic loads or from mechanical aspects of generators. The existence of forced oscillations may affect the estimation accuracy of the natural modes from some measurement based estimation methods. They can also have a dangerous effect on generators caused by resonance between forced oscillations and local modes.;In the first part of this dissertation (chapters 2-3), two new stochastic subspace method are developed and introduced for the power system problem: Recursive Adaptive Stochastic Subspace Identification (RASSI) and Distributed Recursive Stochastic Subspace Identification (DRSSI). In the second part (chapter 4), the concept of resonance in power systems from Forced Oscillations (FO) is discussed and studied.