Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping - Arif,J; Ray,S; Chaudhuri,B (IEEE Transactions on Control Systems Technology, 2013)
The objective of this brief is to design ameasurement-based self-tuning controller, which does not rely onaccurate models and deals with nonlinearities in system response.A special form of neural network (NN) model called feedbacklinearizable NN (FLNN) compatible with feedback linearizationtechnique is proposed for representation of nonlinear powersystems behavior. Levenberg–Marquardt (LM) is applied in batchmode to improve the model estimation. A time-varying feedbacklinearization controller (FBLC) is employed in conjunction withthe FLNN–LM estimator to generate the control signal. Validationof the performance of proposed algorithm is done throughthe modeling and simulating both normal and heavy loadingof transmission lines, when the nonlinearities are pronounced.Case studies on a large-scale 16-machine five-area power systemare reported for different power flow scenarios, to prove thesuperiority of proposed scheme against a conventional modelbasedcontroller. A coefficient vector for FBLC is derivedand used online at each time instant, to enhance the dampingperformance of controller.