This thesis research has designed and developed an optimal predictive excitation control, named the Model Predictive Excitation Control (MPEC), for utility generators. Four significant results are achieved: First, the MPEC has been designed and has significantly improved the classical model predictive control and is much simpler and computationally efficient. Second, the MPEC simulation program and results have been accomplished, and study cases have demonstrated the effectiveness of the MPEC. Third, the Modified classical model predictive control procedure has been formulated to correct a timing error such that the controlling input for the present time is re-written as that for the next step. Fourth, the MPEC optimization formulation and procedure has been developed for the generator control with only two substation-ready-available measurements which are the generator terminal voltage and speed.
CALCIUM-PERMEABLE KAINATE AND AMPA RECEPTORS OF HIPPOCAMPAL GABA-ERGIC NEURONS IN THE EXCITATION CONTROL OF THE NEURONAL NETWORK
