Experimental modeling and control design of shunt active power filters

One of the main issues when designing a control strategy for a power electronic system is the development of a reliable model of the real system. However, the evaluation of the actual plant parameters is difficult due to the mismatch between nameplate and actual values of components, and the presence of unmodeled dynamics and non-linearities. This paper presents a novel technique for both model parameters identification and optimized control design of a shunt active power filter system using genetic algorithms (GAs). Experimental results demonstrate that the proposed modeling and control design approach greatly improve the system performance.