Intelligent Fault Recovery Controller for Power Generator at Perdawd CCGS in Kurdistan-Iraq

Abstract

Different Artificial Intelligent (AI) tools are becoming commonly used to design different intelligent controllers. The objective of this work is to investigate the performance of a suggested intelligent controller of a specific power generation system during and after sadden faults. Two types of AI tools, genetic algorithms and adaptive neuro-fuzzy inference system, have been used in this work to design a control unit for the power station mentioned above as a case study. Simulated models were designed to optimize the control parameters to enhance the performance. A wide range of training data pairs was used to train the controllers and then  to test the system performance. A comparison between the performance of the intelligent and conventional controllers has been introduced. The simulation results show a clear prove that the intelligent controller is outperform the classical one especially during the fault recovery time. Damping performance, oscillation, the system stability and the dynamic performance of electrical power system have been studied.