Enhanced grid stability and prolonging life span in renewable energy power converters using an advanced Sugeno-type AI-based neuro-fuzzy control

Abstract A significant challenge lies in renewable energy sources incapacity to generate high voltages and their limited life spans when subjected to high-ripple conditions. This study introduces an innovative Sugeno-type neuro-fuzzy controller for an interleaved power converter configuration aimed at mitigating the input current ripples associated with these renewable energy sources, directly addressing the longevity concern controlled by an advanced neuro-fuzzy controller. The proposed converter employs a switched capacitor (SC) cell to amplify the generated voltage within the boost converter framework. Key attributes of the proposed converter include high voltage gain, enhanced efficiency and the utilization of short-duty ratio time intervals to minimize conduction power losses at elevated voltages. Furthermore, through interleaved configuration, the current ripple from the source is diminished while the SC cell concurrently amplifies the voltage gain. A Sugeno-type neuro-fuzzy control method, based on artificial intelligence, is employed for the proposed converter to drive the switches and produce an accurate output voltage. Since the converter is primarily built on a fuzzy controller, the proposed method is mathematically simple and easy to implement. The main contribution of the proposed control approach lies in the sampling of both the input and reference, as well as the output voltages, and the generation of precise duty cycles based on the sampled reference output voltage. Due to its capability of generating high voltages, the proposed converter and control system are suitable for use in DC grids and vehicle-to-grid applications.