The objective of this brief is to design a
measurement-based self-tuning controller, which does not rely on
accurate models and deals with nonlinearities in system response.
A special form of neural network (NN) model called feedback
linearizable NN (FLNN) compatible with feedback linearization
technique is proposed for representation of nonlinear power
systems behavior. Levenberg–Marquardt (LM) is applied in batch
mode to improve the model estimation. A time-varying feedback
linearization controller (FBLC) is employed in conjunction with
the FLNN–LM estimator to generate the control signal. Validation
of the performance of proposed algorithm is done through
the modeling and simulating both normal and heavy loading
of transmission lines, when the nonlinearities are pronounced.
Case studies on a large-scale 16-machine five-area power system
are reported for different power flow scenarios, to prove the
superiority of proposed scheme against a conventional modelbased
controller. A coefficient vector for FBLC is derived
and used online at each time instant, to enhance the damping
performance of controller.