A Deep Reinforcement Learning-Based Intelligent Grid-Forming Inverter for Inertia Synthesis by Impedance Emulation
Mohsen Eskandari, Andrey V. Savkin, John Fletcher
Abstract
In this letter, impedance emulation is exploited for synthesizing inertia in autonomous microgrids. An intelligent grid-forming inverter (GFI) is proposed that facilitates sufficient degrees of freedom for adaptive impedance shaping. The latter adaptively changes the effective bandwidth of the inverter's voltage controller, in response to disturbances for inertia synthesis. Deep reinforcement learning is utilized to tackle the lack of explicit quantitative relation between impedance shaping and inertia. Simulation results prove the effectiveness of the method.
Topics & Concepts
EmulationInertiaReinforcement learningElectrical impedanceInverterControl theory (sociology)Computer scienceGridBandwidth (computing)Impedance controlController (irrigation)Control engineeringVoltageEngineeringElectronic engineeringArtificial intelligenceElectrical engineeringMathematicsTelecommunicationsControl (management)PhysicsClassical mechanicsAgronomyGeometryBiologyEconomic growthEconomicsMicrogrid Control and OptimizationSmart Grid Energy ManagementWind Turbine Control Systems