Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping

TL;DR: A special form of neural network model called feedback linearizable NN compatible with feedback linearization technique is proposed for representation of nonlinear power systems behavior and coefficient vector Λ for FBLC is derived and used online at each time instant, to enhance the damping performance of controller.

Abstract: 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 model-based controller. A coefficient vector $\Lambda$ for FBLC is derived and used online at each time instant, to enhance the damping performance of controller.