Model And Control A Three-Area Hydro-Power System Consid Ering The Non-Linearity Using Fuz

Abstract

This thesis presents an analysis of the dynamic performance of load frequency Control (LFC) of three areas of interconnected hydropower systems by the use of a fuzzy type-2 logic con troller, neural network controller, and fuzzy type-2 neuro controller. In this thesis, all three areas consist of the hydropower plant. The application of intelligent controllers such as fuzzy type-2, artificial neural network (ANN), and fuzzy type-2 neuro controllers was explored to improve the efficiency of hydro power system controllers considering the nonlinearities of the system. Intelligent controllers can adapt highly to changing conditions and make decisions quickly by processing imprecise information. They have higher response time, high efficiency, and good handling of system nonlinearities, and are suitable for complex systems. A comparison of Fuzzy type-2, ANN, and Fuzzy type-2 neuro controller-based approaches shows the superiority of the proposed Fuzzy type-2 neuro-based approach over ANN and Fuzzy type-2 for the same conditions. The simulation results are also tabulated as a comparative performance given settling time, peak time, overshoot, and frequency deviations. In comparison to the three-area operation fuzzy type-2 neuro controller, had a smaller percentage overshoot of 0.625%, a settling time of 2.0444sec, and a peak time of 0.2242sec.