Tuning Phase Lock Loop Controller of Grid Following Inverters by Reinforcement Learning to Support Networked Microgrid Operations

The dynamic operation of networked microgrids leads to varying topological configurations and generator commitments and dispatches. These variations correspond to systems with different electrical characteristics. The fixed control gains of high-speed power electronic devices may result in undesirable system performance when the electrical characteristics change significantly. As such, it is necessary to tune the control gains of power electronics devices to adapt to the changing system characteristics. This paper uses observer-based reinforcement learning to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing system strengths, that would be seen in networked microgrid operations. Simulation results using a 26kV electric distribution system, modeled as networked microgrids, are presented to demonstrate the need and effectiveness of the proposed adaptive controls.

Automated summary

The dynamic operation of networked microgrids leads to variations in topological configurations and generator commitments, corresponding to systems with different electrical characteristics. Fixed control gains of power electronic devices may result in undesirable system performance when electrical characteristics change significantly. Therefore, this paper proposes using observer-based reinforcement learning to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing system strengths in networked microgrid operations. Simulation results using a modeled 26kV electric distribution system demonstrate the need and effectiveness of the proposed adaptive controls.