Passivity-Based Grid-Forming Control Strategy and Parameter Optimization for Grid-Connected Converters Using the PSO Algorithm

As the primary interface linking renewable energy sources to the power grid, the control performance of grid-connected inverters is critical for ensuring system stability. Traditional linear controllers such as PI struggle to adapt to the random, nonlinear variations in grid structure and parameters caused by large-scale renewable integration, often lacking voltage and frequency support, which can lead to current and voltage oscillations or even instability. Passivity-based nonlinear control offers a promising solution from an energy regulation perspective. However, conventional passivity controllers are model-dependent and require detailed knowledge of system parameters. Their performance often relies on manual, trial-and-error tuning of damping gains, limiting robustness under complex grid conditions. To address this, we propose a stability control strategy and parameter design method for grid-forming inverters based on passivity theory. To enhance dynamic performance and robustness, the damping parameters of the passivity controller are optimized using the particle swarm optimization (PSO) algorithm. Simulation results verify the correctness and effectiveness of the proposed control strategy and optimization method for grid-forming inverters under scenarios of high renewable penetration and nonlinear grid disturbances.