| dc.description.abstract | The efficiency of an induction motor (IM) can be improved by the optimum
selection of a flux level in the motor. Among the numerous loss minimization
algorithms (LMA), a loss-model-based approach offers a fast response and no torque
pulsation. However, it requires the accurate loss model and the knowledge of the
motor parameters to find the optimum flux level. Therefore, a technical difficulty in
deriving the loss-model-based LMA lies in the complexity of the full loss model and
the on-line parameter adaptation for the precise motor parameters.
In an effort to overcome the drawbacks of on-line loss model controllers (LMC),
this thesis presents a new loss-model-based LMA for inverter-fed IM drives aiming at
both high efficiency and high dynamic performance.
A new LMC is proposed for the loss minimization of vector-controlled IM drives.
An IM model in d-q coordinates is referenced to the rotor magnetizing current and
then an iron loss resistance is added in parallel to the magnetizing inductance. This
transformation leads to no leakage inductance on the rotor side by incorporating it
into other parameters. This decomposition feature into d-q components makes the
derivation o f the motor loss model and LMC simpler while keeping the effect of
leakage inductances.
In order to achieve high dynamic performance, an adaptive backstepping based
nonlinear controller (ABNC) is designed incorporating iron loss under the parameter
uncertainties of rotor resistance and load torque. In proposed IM equations, no
additional state variables are added while iron loss is considered. Thus, an ABNC incorporating iron loss can be designed without much m ore complexity compared to
the one with neglected iron loss. ABNC achieves desirable motor dynamics at any
operating point while the flux level is varied by the LMC in order to reduce the input
power.
Adaptive backstepping technique provides a tool to design the controller avoiding
wasteful cancellations of certain nonlinearities. Another important feature of an
adaptive backstepping technique is that it can derive param eter update laws
simultaneously with control laws from the error dynamics. With an extra gain
introduced in adaptation laws design, we take advantage of this feature by combining
the ABNC with LMC, thus an on-line param eter adaptation of LMC can be obtained
with no extra effort.
The complete closed loop control o f the proposed LMC based IM drive is
implemented in real-time using digital signal processor board DS 1104 for a
laboratory 1/3 hp motor. The dynamic performance of the proposed controller and
parameter adaptation features are examined. The effectiveness of the proposed loss
minimization scheme through a wide range of speed regions including the field
weakening region is demonstrated through computer simulation and experimental
results. | |
