Transient Stability Analysis of Distributed Systems with Distributed Generation (DG)

Transient stability analysis of distributed systems with Distributed Generation (DG) represents a critical research direction in power systems. The integration of renewable energy sources into modern power systems drives the transformation from conventional grid architectures toward distributed system topologies. The incorporation of DG introduces novel challenges to system stability, particularly during transient conditions.

Transient stability analysis primarily investigates a system's ability to return to stable operation following disturbances (e.g., short-circuit faults, sudden load changes). Since DG typically connects to the grid through power electronics inverters, their dynamic characteristics differ significantly from conventional synchronous generators, necessitating special attention to DG's impact on transient stability. Implementing such analysis typically requires simulating inverter dynamics using differential-algebraic equations and control system blocks in tools like MATLAB/Simulink.

Key considerations for DG transient stability analysis include: Inverter Control Strategies: DG control methods (e.g., PQ control for constant power output, V/f control for voltage and frequency regulation) significantly influence system dynamic response. Code implementation often involves PID controllers and reference frame transformations (dq-to-abc). System Inertia Reduction: DG displacement of conventional generation may reduce system inertia, impacting frequency stability. Simulation models need to incorporate swing equations with modified inertia constants. Fault Ride-Through Capability: DG behavior during grid faults (e.g., low-voltage ride-through support) affects transient recovery. This requires implementing voltage dip detection algorithms and current limiting logic in control code. Protection Coordination: DG integration may alter protection system characteristics, necessitating reevaluation of protection coordination strategies. This involves modeling overcurrent relays with adaptive settings in coordination studies.

Analytical methods typically include time-domain simulations (implemented using numerical integration methods like Runge-Kutta), Lyapunov theory-based stability criteria (requiring energy function calculations), and artificial intelligence-assisted assessments (using machine learning classifiers for stability prediction). Future developments in microgrids and smart grids will emphasize multi-energy coordination and optimization of fast control algorithms, potentially implemented through model predictive control (MPC) frameworks.