An Adaptive Data-Driven-Based Control for Voltage Control Loop of Grid-Forming Converters in Variable Inertia MGs - Watcharakorn Pinthurat; Prayad Kongsuk; Tossaporn Surinkaew; Boonruang Marungsri (My University, 2024)
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. Simulation results and case studies validate the efficacy of the adaptive data-driven control technique in optimizing voltage control within variable-inertia MGs with high penetration RESs and DERs. This innovative approach represents a significant stride towards fully controlling RESs and DERs to support MG's voltage without requiring exact MG's parameters, thereby ensuring grid stability and power quality, and supporting the transition towards sustainable energy systems.
Keywords: Microgrid; Model Predictive Control; Adaptability; Microgrid Control and Optimization