Parameter estimation using an orthogonal series expansion

This paper presents an innovative alternative to estimate parameters of a system for which a dynamic model is known. The focus of this paper is the esti mation of the armature circuit parameters of large utility generators using real time operating data. Other applications are possible. The alternatives considered are the use of orthogonal series expansions, in general, and the Hartley series, in particular. The main idea considers the use of orthogonal series expansions for fitting operating data (e.g., voltage and currents measurements). This allows writing a set of linear algebraic equations that can be "solved" in the least squares sense for the unknown parameters. The method shown utilizes the pseudoinverse in the solution. The essence of the approach is linear state estimation. Several alternative types of orthogonal expansions are briefly discussed. Although solutions are the same in all domains, one wishes to employ the expansion that gives the most efficient computation. The approach may be used for static as well as dynamic problems. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.