Application of genetic algorithms in control design for advanced static VAR compensator at ac/dc interconnection

Abstract

Several aspects of design of the Advanced Static VAR Compensator (ASVC), and design of a control system for optimum performance of the ASVC at an ac/dc interconnection are presented in the thesis. One of the most important aspects of the designing process of the ASVC is reduction of harmonic components in the ASVC output voltage. To meet a requirement regarding the maximum contents of harmonics in the voltage a configuration of the ASVC and the size of its components have to be properly selected. A method based on harmonic analysis has been used to design the ASVC power circuit. The importance of such analysis under both balanced and unbalanced conditions have been identified. A new design methodology of control systems is proposed here. It relies on a combination of advanced system simulator - $EMTDC\sp{TM}$ and genetic computation. In contrast with standard approaches used in control systems design, this framework provides realistic full-scale modeling abilities accomplished via the simulator along with the optimization versatility of Genetic Algorithms. This new methodology has been used to design a control of the ASVC. Using capabilities of genetic computation for multiobjective and multivariable optimization a structure of a control system has been designed and the values of control parameters have been adjusted. A concept of switching control has also been investigated. A fuzzy controlled switching between two sets of parameter values has been used to design an ASVC control for the case of the inverter close-in single phase to ground fault. The results show a better performance of the ASVC when compared to its performance with a standard type of control. A control scheme using different sets of control parameter values in a control procedure, depending on the state of a system, has been used to investigate the performance of the ASVC at an ac/dc interconnection. Using this approach the values of control parameters which are optimal for a given event are used.