Performance optimization of a STATCOM based on cascaded multi-level converter topology using multi-objective Genetic Algorithm

In this paper, a comprehensive study in component sizing of a cascaded multi-level inverter based static synchronous compensator (STATCOM) is presented. Multi-objective Genetic Algorithm (MOGA) is utilized to optimize the performance of STATCOM. “Equivalent dynamic capacitance” concept is considered in order to simplify the complex structure of the multi-level inverter based STATCOM. Cascaded multi-level inverter parameters such as DC capacitance, initial capacitor charge, switching angles and also power system parameters including buffer inductance, transformer ratio and transformer inductance are to be found using optimization algorithm. In contrast with previous researches which have tried to optimize the objectives independently, in this paper the aim of using MOGA is optimizing losses, total harmonic distortion (THD) and maximum DC voltage ripple simultaneously, providing designing constraints with respect to the “equivalent dynamic capacitance” concept. The results show that better performance of STATCOM can be achieved via a thorough analysis by considering all parameters of STATCOM and components of power system as decision variables of a multi-objective optimization problem.