A recent frequency-domain, subspace-based algorithm as well as
the well-known
nonlinear least-squares algorithm are used in the identification of a
power transformer whose frequency response has a
dynamic range of $1$MHz. When the model complexity is not restricted,
both the algorithms produce highly accurate models. Low complexity
models are extracted from the high order identified ones via the
method of balanced truncation. It is observed that this two-step
procedure yields more accurate results than an approach of direct identification
of a low order model. The utility of identified models for the purpose
of transformer fault detection is also briefly discussed.