A Data-Driven Model-Free Predictive Voltage Control Strategy for Grid-Forming Inverters

The traditional model predictive voltage control (MPVC) for grid-forming inverters relies heavily on accurate system parameters, which can impact voltage prediction performance. To address this issue, this paper proposes a data-driven model-free predictive voltage control strategy (DD-MFPVC). Initially, the influence of system parameters on traditional MPVC is analyzed for grid-forming inverters. Then, the data-driven model for grid-forming inverters is established, which is identified using the least-squares method. Furthermore, by applying the multi-layer recursive principle, the future data-driven model is derived from historical data, with recursive coefficients estimated using least squares to mitigate the one-step delay effect in the data-driven model. Subsequently, the voltage vector reference for the inverter is calculated based on deadbeat control principles, integrating space vector modulation to ensure voltage prediction accuracy. The proposed DD-MFPVC allows real-time updating of the data-driven model, eliminating dependency on system accuracy, and reducing prediction errors. The effectiveness of the proposed DD-MFPVC is validated through experimental comparisons. © 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.