Study of Bidirectional AC/DC converter with feedforward scheme using Neural Network Control in Microgrid System

This paper presents the study of bidirectional ac/dc converter PWM with feedforward neural network control. Bidirectional ac/dc converter act as a utility interface between ac grid and distributed energy resources or renewable energy resources. The converter facilitates a battery energy system for power charge or discharge to compensate for the dc bus voltage deviation during severe distribution conditions. Due to these disturbances such as fault occurrences, system loads and varying environmental condition causes overshoot and undershoot problem. This proposed system reduces the overshoot and undershoot to very low value. Current harmonics in a PWM bidirectional ac/dc power converter are reduced considerably by using feedforward neural network. This paper present a modified feedforward technique having neural network and existing model with feedforward technique having PI controller will be compared. Both schemes are explain with experimental result. The proposed simplified PWM provides the better voltage regulation and high fundamental dc output voltage with lower THD and high efficiency. This proposed project has larger fundamental output voltage in inverter mode. Both simulation and experimental results verify the validity of the proposed PWM strategy and control scheme.