Artificial neural network-based virtual synchronous generator for frequency stability improving of grid integrating distributed generators

Abstract

The integration of renewable energy sources (RESs) is becoming increasingly prevalent in contemporary power grids. RESs, including distributed generators (DGs), utilize power electronics converters to interface with the grid, contributing to a reduction in grid inertia and an increase in vulnerability to stability issues. This shift has led to a gradual displacement of the traditional role of synchronous generators (SGs) in providing frequency regulation, with power electronics converters such as inverters taking on a more prominent role. Virtual synchronous generators (VSGs) or virtual synchronous machines (VSMs) offer a solution by emulating SG behavior in power electronics converters. However, these techniques encounter limitations in mathematical calculations and precision. This article proposes an artificial intelligent based VSM controller (AIVSM) designed to overcome these limitations. The AIVSM system leverages artificial neural networks (ANNs) to emulate real SGs. The ANN is trained using a substantial dataset derived from a SG of a diesel generator. Simulation results demonstrate the performance superiority of the AIVSM when compared to a conventional proportional integral (PI) VSM controller and an adaptive VSM controller.