This paper presents an innovative alternative to estimate parameters of a s
ystem for which a dynamic model is known. The focus Of this paper is the es
timation, of the armature circuit parameters of large utility generators us
ing real time operating data. Other applications are possible. The alternat
ives considered are the use of orthogonal series expansions, in general, an
d the Hartley series, in particular. The main idea considers the use of ort
hogonal series expansions for fitting operating data (e.g., voltage and cur
rents measurements). This allows writing a set of linear algebraic equation
s that can be "solved" in the least squares sense for the unknown parameter
s. The method shown utilizes the pseudoinverse in the solution. The essence
of the approach is linear state estimation. Several alternative types of o
rthogonal expansions are briefly discussed. Although solutions are the same
in all domains, one wishes to employ the expansion that gives the most eff
icient 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 dig
ital fault recorder data to identify synchronous machine parameters.