COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS, vol.3512, pp.1108-1115, 2005 (SCI-Expanded)
This paper proposes a new controller based on neural network and fuzzy logic technologies for load frequency control to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A "Dynamical Fuzzy Network (DFN)" that contains dynamical elements such as delayers or integrators in their processing units is used in the adaptive controller design for load frequency control. A DFN is connected between the two area power systems. The input signals of the DFN are the ACEs and their changes. The outputs of the DFN are the control signals for the two area load frequency control. Adaptation is based on adjusting parameters of DFN for load frequency control. This is done by minimizing the cost functional of load frequency errors. The cost gradients with respect to the network parameters are calculated by adjoint sensitivity. In this paper, it is illustrated that this control approach is more successful than conventional integral controller for load frequency control in two area systems.