Optimizing Grid-Forming Controls using Relay-based Extremum Seeking to Enhance Transient Performance
This work addresses the need for adaptive control parameter tuning in grid-forming inverters to enhance power system stability under varying conditions, though it is an incremental application of existing ESC methods to a specific power system problem.
The paper proposes a real-time adaptive optimization framework using Extremum Seeking Control to dynamically tune the droop coefficient of grid-forming inverters, balancing competing transient performance objectives. Simulations on a modified IEEE 68-bus system show the cost function is convex and the algorithm tracks the time-varying optimal droop coefficient in real-time.
Grid-forming (GFM) inverters are essential for enhancing stability in modern power systems with high penetration of inverter-based resources (IBRs). However, their performance highly depends on control parameters tuning, particularly the active power-frequency droop coefficient. This parameter presents a trade-off among competing objectives, including damping, settling time, rate of change of frequencies (RoCoF) and frequency nadirs. This paper proposes a real-time, adaptive optimization framework based on Extremum Seeking Control (ESC) to dynamically tune the GFM droop gain. A multi-objective cost function balances conflicting performance goals such as oscillation energy, frequency nadir, RoCoF, and post-disturbance settling performance. The approach is validated through numerical simulations on a modified IEEE 68-bus system. Results demonstrate that the cost function is convex with respect to the droop parameter, justifying gradient-based optimization. Furthermore, the ESC algorithm successfully tracks the time-varying optimal droop coefficient in real-time as network conditions change, thereby ensuring robust and near-optimal system performance without requiring an analytical grid model.