Application of genetic algorithms in control design for advanced static VAR compensator at AC/DC interconnection

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 - {dollar}EMTDCsp{lcub}TM{rcub}{dollar} 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.