Comparative Analysis of Intelligent Control Systems for Frequency and Power Control of Gilgel Gibe 2 Hydroelectric Power Plant

Abstract

Power plants have particular control systems to ensure stable operation. The satisfactory operation of a power system requires a frequency control that keeps it within acceptable limits when the system is subjected to significant load variation. With the concept of that the recent developments in the power sector, conventional controllers are designed considering linear power system models; therefore the performance of these controllers may not suffice if a controlled system is of high order and nonlinear for the requirements of power and frequency control. New strategies and controllers for controlling and monitoring hydro power generation are developed to upgrade the conventional controllers. To address these complex problems systematically, several methods have been developed in recent years that are collectively known as “intelligent control” methodologies. The main goal of this thesis is on analyzing and comparing an intelligent control system for frequency and power control of the Gilgel Gibe 2 HEPP in the networked and isolated mode of operation that has been investigated during operational set point changes. A particle swarm optimization which is robust in solving continuous non-linear optimization problems and fuzzy logic controller for compare and contrast the conventional proportional integral derivative governor control in which active power can be designed with Frequency and Power control of GG2 HEPP. The simulation has been performed for different operating conditions using a dynamic model of the power plant on MATLAB Simulink software to analyze and select the best controller response comparatively. The simulation result shows that the overall system output performance is improved 97.5% of its settling time using the proposed particle swarm optimization by tuning the existing PID controller comparatively from fuzzy logic and the existing PID controller. When one percent in frequency is increased, the load is drooped from the system network, and the control response shows that PID Tuned by particle swarm optimization resulted in less than 1.243% overshoot, fewer settling times at 0.4052sec, and fewer rising times at 0.0034sec as compared to the fuzzy logic controller and existing PID controller. This controller helps to enhance the power quality of the power plants.