Model Reduction Methods Applied to Power Systems

Abstract

<p>This thesis presents a continuation in the process of rationalizing, unifying and improving existing model reduction techniques. Thus a method of reduction is developed which combines the method of aggregation and partial Pade approximation in such a way as to maintain their separate advantages while simultaneously removing their disadvantages. The important aspects associated with the reduced-order models obtained are: guaranteeing the stability of the reduced-order models saving computation time, retaining the invariance property under state variable feedback conditions and matching some of the original system time moments.</p> <p>Also, a criterion is proposed for selecting the state variables of the original system to be retained in the reduced-order model. This criterion leads to developing a reduction technique which can be regarded as a combination of the methods of aggregation and singular perturbation. Therefore, the reduced-order model obtained retains the physical significance of the state variables and the dominant eigenvalues of the original system.</p> <p>Furthermore, a procedure is developed for obtaining dynamic equivalents of multimachine systems. This procedure utilizes the concept of component cost analysis for identifying the coherent groups of generators.</p> <p>Verification of the methods developed in the thesis is established using a variety of realistic power system models including a single synchronous machine connected to an infinite bus, a three-machine system and a 10-machine system. These applications include simulation, analysis and simple controller design.</p>