Genetic algorithm for optimal capacitor selection and optimal power flow with FACTS devices

The main contributions of this research work are thus to develop computer algorithms using Genetic Algorithms (GA) to solve the optimal capacitor allocation problem with harmonic distortion considerations and the optimal control setting problem of FACTS devices in optimal power flow (OPF).; The first part of this thesis reports the research findings of a genetic algorithm approach for optimizing shunt capacitor sizes and their placement in radial distribution systems with the consideration of harmonic distortion limit due to the presence of nonlinear power electronic devices. The algorithm is based on a genetic algorithm (GA) solution technique to minimize cost under the additional constraints of maximum limit in Harmonic Distortion Factor (HDF) and voltage.; Secondly, the thesis would present the development of the equivalent modeling by Power Injected Method (PIM) of various types of FACTS devices including Thyristor Controlled Series Compensator (TCSC), Thyristor Controlled Phase Shifter (TCPS) and Unified Power Flow Controller (UPFC). A real-coded genetic algorithm method is presented to solve the optimal power flow problem of power system with flexible AC transmission systems (FACTS). The proposed method introduces the injected power model of FACTS devices into Newton-Raphson (NR) power flow problem to exploit the characteristic of FACTS devices.; In the developed methodology, GA effectively finds the optimal setting of the control parameters using the conventional OPF method as an embedded calculation tool. Overall, the results show that GA is suitable in dealing with non-continuous, nondifferentiable and non-convex optimization problems, such as the capacitor selection and optimal power flow problems with FACTS devices.