an investigation of dual stator winding induction machines

most recent work on this estimation algorithm includes an improved closed-loop stator
flux estimator which adjusts the value of stator resistance on-line [10.19]. The cross
product of estimated and actual currents is utilized in the resistance estimation. Almost
starting at the same time, another adaptive estimation methodology called full-order flux
observer has been presented in [10.20-10.23]. In [10.20], the Lyapunov’s theorem was
utilized to derive the adaptive scheme, which is similar to the error function in [10.17].
Since the adaptive control theorem is applied to both estimation algorithms, the boundary
between them is not clear and both of these estimation schemes can be considered to be
one although some authors call it MRAS-based speed estimation and the others call it a
full-order flux observer based speed estimation as found in many publications in this
area. In this dissertation, both speed estimation algorithms will be combined and called
MRAS-based speed estimation scheme.
From the definitions of reference model and adaptive model point of view, the
MRAS-based speed estimation schemes are broadly classified into two categories. In the
first type, the rotor speed independent stator equations are used as a reference model
while the rotor speed dependent rotor equation is assumed to be an adaptive model. The
errors between these models are used to formulate an error function or adaptive scheme,
which passes through a PI controller and yields the estimated rotor speed [10.17, 10.18,
10.19, 10.24]. However, the actual machine itself is considered to be the reference model
and the flux observer becomes the adaptive model in the second approach. The error
between the measured stator currents from reference model and the estimated stator
currents from adaptive model are combined with rotor fluxes to be the error function
[10.20-10.21]. This MRAS scheme is called full-order flux observer based speed
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