An Adaptive Data-Driven-Based Control for Voltage Control Loop of Grid-Forming Converters in Variable Inertia MGs

Abstract

In the transition towards sustainable energy systems, microgrid (MG) plays a pivotal role, especially in the context of variable-inertia MGs that integrate renewable energy sources (RESs) and distributed energy resources (DERs). Maintaining stable voltage control within such grids is imperative for reliable operation. This paper presents an adaptive data-driven control technique for the voltage control loop of grid-forming converters in variable-inertia MGs. The primary objective is to enhance control performance while accommodating the unpredictable nature of renewable energy sources. The approach utilizes advanced data-driven algorithms to continuously monitor and adjust control parameters based on real-time grid conditions. This adaptability allows for effective management of varying inertia and load demand, ensuring optimal grid performance. The data-driven nature of the approach enables self-adaptability, making it suitable for the dynamic MG environment. This new approach is a noteworthy advancement in controlling RESs and DERs to maintain stable voltage in MGs, without needing precise knowledge of the MG's parameters. Simulation tests and real-world examples confirm that the adaptive data-driven control method effectively optimizes voltage control in MGs with variable inertia, especially those with a high presence of RESs and DERs.