Power System Critical Eigenvalue Estimation Using Flexibility of Subspace System Identification

Abstract

In this research, a new algorithm and proposition are suggested to estimate critical eigenvalues of power systems. The algorithm is based on the characteristics of five renowned deterministic subspace system identification algorithms: the multivariable output error state space, past output multivariable output error state space, numerical algorithm for subspace identification, canonical correlation analysis, and orthogonal decomposition technique. Suggesting a novel proposition, the mentioned subspace system identification algorithms are reformulated in a general realization framework in order to clarify their similarities and differences. Numerous implementation characteristics of the algorithms are explored in order to automate system order selection and state matrix estimation. Performance of the proposed algorithm, when applied for eigenvalue estimation, is investigated in the concern of estimation accuracy and computational loads. Different aspects of the proposition and the algorithm are explored through conducting several simulations. The simulations may be carried out using a simple test power system (two-area four-machine system), an average system (New England 10-machine system) and a complicated power system benchmark (IEEE 50-machine system). In accordance with the test results, it is claimed that among the investigated subspace system identification algorithms, the past output multivariable output error state space has very good performance for power system eigenvalue, damping ratio, and frequency estimation when concerning estimation accuracy and CPU processing time.

Keywords:

• power system
• deterministic subspace system identification
• eigenvalue estimation
• damping and frequency estimation