Neural block control for synchronous generators
In this paper, a novel approach to control a single generator, connected to an infinite bus, is presented. Modifying published results for nonlinear identification using recurrent neural networks, a block controllable neural identifier is proposed, based on this neural model a control law is derived, which combines sliding modes and block control. The neural identifier and the proposed control law allows to reject external disturbances caused by generator terminal short circuits and mechanical power variations. Applicability of the approach is tested via simulations.
neural networks power-energy electro-mechanical systems adaptive control gray-box modeling