Improved Adaptive Network-Based Fuzzy Inference System Approach for Power Flow Analysis in Interconnected DC Microgrid

Abstract

High-performance power conversion needs are becoming more and more important for microgrid applications. In actuality, many contemporary DC distribution systems use isolated bidirectional dc-dc converters. Hence, in this paper, an Improved Adaptive Network-based Fuzzy Inference System (IANFIS)is developed to control the output power of the components and reduce the variation among the consumption and generation of the power while managing a constant DC bus voltage. This work proposes a power management control strategy that ensures the power balance of a stand-alone DC microgrid where DAB converters link the battery energy storage (BES) unit and the renewable energy source (RES). The generation side, or photovoltaic (PV) system and BES, is coupled to the storage systems and renewable energy sources. Furthermore, a DC microgrid is connected to the load demand. The load demand is compensated with the help of the storage and generation systems with the help of a dual active bridge converter. The DAB is connected with the dual DC microgrid system for managing power flow among the two DC microgrids. This dual active bridge converter is utilized to balance the power among Dual DC microgrids. The proposed methodology is developed to manage the power among generation and load demand management.  The proposed method is evaluated on a dual active bridge converter connected microgrid system with the components of PV and BESS respectively.