Neural Network Based Voltage Source Converter for Power Management of Hybrid Energy System

This paper proposes, the Controlling and co-ordination of energy management in different power generation or resources is always complex task in hybrid micro grid. To integrate renewable energy sources like solar-wind and battery multiple power converters usage required. The overall performance of the hybrid microgrid power system network is dependent on the operating of the power converters. Voltage Source Converters (VSCs) can optimize power flow within a micro grid or power system by providing advanced control capabilities. VSCs offer a range of control capabilities that can be leveraged to optimize power flow within micro grids and power systems. Their ability to regulate voltage, control reactive power, and respond dynamically to changes in the system makes them valuable components for improving efficiency and reliability in various operational scenarios. In this paper an Artificial Neural Network (ANN) controller is developed to operate the single phase VSC converter at load side. To reach the load demand and enhance the power quality battery supported is considered to the solar and wind power generation. The performance of the proposed ANN based hybrid microgrid is investigated on two scenarios such as steady load connected to the variable solar, wind power generation and performance of the system when dynamic load is connected at constant PV and wind power generations.

Keywords: — Energy Management Systems, Hybrid power generation, Neural networks