Dynamic Modeling and Stability Analysis for Repeated LVRT Process of Wind Turbine Based on Switched System Theory

TL;DR

This work addresses voltage oscillations at grid-connected wind farms caused by repeated LVRT in weak grids (SCR $<2.0$). It develops a WT-GSC switched-system representation that captures external impedance and internal control dynamics across normal and LVRT modes, and analyzes stability via a common Lyapunov function-based criterion. A Sobol' global sensitivity analysis identifies dominant parameters which are then optimized with a PSO algorithm to maximize a stability index $\mu$, with validation on a modified IEEE 39-bus system showing improved stability and reduced voltage fluctuations. The framework provides a quantitative, design-oriented tool for enhancing the robustness of grid-connected wind turbines under LVRT switching in weak grids.

Abstract

The significant electrical distance between wind power collection points and the main grid poses challenges for weak grid-connected wind power systems. A new type of voltage oscillation phenomenon induced by repeated low voltage ride-through (LVRT) of the wind turbine has been observed, threatening the safe and stable operation of such power systems. Therefore, exploring dynamic evolution mechanisms and developing stability analysis approaches for this phenomenon have become pressing imperatives. This paper introduces switched system theory for dynamic modeling, mechanism elucidation, and stability analysis of the repeated LVRT process. Firstly, considering the external connection impedance and internal control dynamics, a novel wind turbine grid-side converter (WT-GSC) switched system model is established to quantitatively characterize the evolution dynamic and mechanism of the voltage oscillation. Subsequently, a sufficient stability criterion and index grounded in the common Lyapunov function are proposed for stability analysis and assessment of the WT-GSC switched system. Moreover, to enhance the system stability, the Sobol' global sensitivity analysis method is adopted to identify dominant parameters, which can be further optimized via the particle swarm optimization (PSO) algorithm. Finally, simulations conducted on a modified IEEE 39-bus test system verify the effectiveness of the proposed dynamic modeling and stability analysis methods.

Figures (10)

• Figure 1: Diagram of a typical switched system.
• Figure 2: Dynamic evolution curves of the WT-GSC switched system during the repeated LVRT process. (a) Phase portrait. (b) Power curve. The blue and orange curves denote the normal operation and LVRT subsystem dynamics, respectively. The blue and orange square points in (a) denote the stable equilibrium points of the normal operation and LVRT subsystems, respectively (i.e., (1.00, 0, 0.79) and (1.00, -0.11, 0.84)).
• Figure 3: Phase portraits of the WT-GSC switched system starting from different initial points. (a) Starting from the zero-state point (0, 0, 0.79). (b) Starting from the stable equilibrium point of the LVRT subsystem (1.00, -0.11, 0.84).
• Figure 4: Phase portraits of the WT-GSC switched system under different working conditions. (a) Blocked LVRT control. (b) Continuous LVRT operation.
• Figure 5: Topology of the modified IEEE 39-bus test system.

Theorems & Definitions (1)

• proof