In this paper, an intelligent approach to synchronous generator control is described. This method combines two controllers, one a neural network based controller with explicit neuro-identifier, and the other an intelligent adaptive controller implemented as a Model Reference Adaptive Controller (MRAC) to perform a hybrid control operation. The neuro-control identifier combination is used to approximate the nonlinear function and the MRAC control adapts when plant parametric set changes. Additionally, a Feed Forward Neural Network (FFNN) identifier is used to predict system response to control values and those values adjusted to obtain improved system response. The FFNN is trained offline with extensive test data, and is also adjusted online. Main advantage and uniqueness of the proposed scheme is the controller's ability to complement each other in case of parametric and functional uncertainty. Moreover, the online neural network produces a plant functional approximation. The theoretical results are validated by conducting simulation studies on a single machine infinite bus system for electric generator control.
['Sukumar Kamalasadan', 'Gerald D. Swann']
An intelligent hybrid controller for speed control and stabilization of synchronous generator