A novel Radial Basis Function Neural Network based intelligent adaptive architecture for Power System Stabilizer

In this paper, we propose a new class of intelligent adaptive control systems based on a system-centric approach for the control of generators under transient operating conditions. The proposed architecture consists of a Model Reference Adaptive Controller (MRAC) operating in parallel with a Radial Basis Function Neural Network (RBFNN) to control generator oscillations in the presence of disturbances. The underlying structural feature is the introduction of an Intelligent Supervisory Loop (ISL) to augment a conventional adaptive controller. The main advantage of this algorithm is that it is precise, feasible, stable, and more effective than other nonlinear adaptive controllers acting alone. Simulation results are presented showing substantial improvement in the oscillatory and transient response of a generator in a Single Machine Infinite Bus (SMIB) while using the proposed control scheme.