The use of power system stabilizers is essential for reliable operation of large electrical
systems. Most stabilizers in operation are designed using classical control techniques based
on linearized power systems models. Although this type of stabilizer presents satisfactory
performance for the damping of oscillations inherent in the power system, many studies show
that use of adaptive and intelligent control techniques for the synthesis of the control law in
these stabilizers can produce even better results.
In this work it is investigated the performance of a predictive control strategy, of the
minimum variance control type in the state space, GMVSS, applied to the damping of
electromechanical oscillations in interconnected power systems. The design procedure is
based on the premise that the controller structure is inherited from the design model, where
estimated state variables, come into play in the synthesis of a state feedback control law. The
complexity of the controller structure is then dictated by the complexity of the design model.
This procedure differs from the original transfer function method, GMV, however matching
exactly the same results. The most significant contribution of such a strategy is the simplicity
of design due to the absence of the Diophantine equation in the procedure. The Diophantine
equation is indirectly solved in a natural way by the problem formulation itself, from a
Kalman filter obtained from an ARMAX state space representation.
Finally, the synthesized control law is applied to the nonlinear system by means of numerical
simulations using nonlinear models of the system, evaluating the characteristics of robustness
and performance of the proposed controller via sensitivity functions, Nyquist diagram, poles
and zeros map and performance indexes for the entire operating range. The results show that
the predictive stabilizer is able to contribute positively to the damping of the most problematic
oscillation modes, thus increasing the stability limits of the power system.
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