Hardware-in-the-Loop Real-Time Transient Emulation of Large-Scale Renewable Energy Installations Based on Hybrid Machine Learning Modeling

Abstract

For the integration of large-scale renewable energy resources into power grids, the complex and dynamic behavior of inverter-based resources (IBRs), such as wind farms, photovoltaic (PV) arrays, and battery energy storage systems (BESSs), poses significant challenges. Traditional models often fall short of feasibly simulating these resources at scale. This article introduces a hybrid machine learning approach, employing multilayer perceptrons (MLPs) and gated recurrent units (GRUs), to effectively simulate IBRs. The hybrid models combine MLPs and GRUs to capture the transients of IBRs. An extensive dataset, including environmental data, load profiles, and fault instances, was used for training and validation. The source of this dataset was the computational electromagnetic transient (EMT) models of IBRs and validated results. A test system was developed to integrate a microgrid comprising batched ML-based IBR modules into a large-scale ac–dc system, which is based on the IEEE 118-bus system. The system is deployed on a field-programmable gate array (FPGA) board, highlighting the viability of real-time, hardware-accelerated emulations. The results show that the hybrid ML methodology accurately represents large-scale IBRs and predicts transient behaviors in integrated grids, offering crucial insights for the future planning, operation, and control of ac–dc grids, especially those with high renewable energy integration.