PSO-based Sliding Mode Current Control of Grid-Forming Inverter in Rotating Frame

TL;DR

This work targets improving current control for Grid-Forming Inverters by optimizing the Decoupled Average-Model Sliding Mode Current Controller (DAM-SMC) with offline Particle Swarm Optimization (PSO). By tuning the DAM-SMC parameters ($k_{sat}$, $k_{cd}$, $k_{cq}$) to minimize the integral absolute error, the approach reduces chattering and tracking error while preserving a fixed switching frequency. Simulation results demonstrate an 11.61% improvement in tracking accuracy over conventional DAM-SMC and substantial reductions in convergence time compared with GA and SA optimizations, under scenarios with load changes and model uncertainties. The method shows robustness to parameter variations up to 40% and offers practical benefit for GFMI implementations, with future plans for hardware-in-the-loop validation and real-time DSP deployment.

Abstract

The Grid-Forming Inverter (GFMI) is an emerging topic that is attracting significant attention from both academic and industrial communities, particularly in the area of control design. The Decoupled Average Model-based Sliding Mode Current Controller (DAM-SMC) has been used to address the need such as fast response, fixed switching frequency, and no overshoot to avoid exceeding current limits. Typically, the control parameters for DAM-SMC are chosen based on expert knowledge and certain assumptions. However, these parameters may not achieve optimized performance due to system dynamics and uncertainties. To address this, this paper proposes a Particle Swarm Optimization (PSO)-based DAM-SMC controller, which inherits the control laws from DAM-SMC but optimizes the control parameters offline using PSO. The main goal is to reduce chattering and achieve smaller tracking errors. The proposed method is compared with other metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Simulated Annealing (SA). Simulations are performed in MATLAB/Simulink across various scenarios to evaluate the effectiveness of the proposed controller. The proposed approach achieves a substantial reduction in convergence time, decreasing it by 86.36% compared to the GA and by 88.89% compared to SA. Furthermore, the tracking error is reduced by 11.61% compared to the conventional DAM-SMC algorithm. The robustness of the proposed method is validated under critical conditions, where plant and control model parameters varied by up to 40%.

Figures (5)

• Figure 1: Configuration of the studied system.
• Figure 2: dq inductor currents under linear load change.
• Figure 3: dq inductor currents under non-linear load.
• Figure 4: dq inductor currents under uncertainty.
• Figure 5: Fitness function for the optimization methods.