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Title: Distributed Model Predictive Control for Autonomous Droop-Controlled Inverter-Based Microgrids
Microgrids must be able to restore voltage and frequency to their reference values during transient events; inverters are used as part of a microgrid's hierarchical control for maintaining power quality. Reviewed methods either do not allow for intuitive trade-off tuning between the objectives of synchronous state restoration, local reference tracking, and disturbance rejection, or do not consider all of these objectives. In this paper, we address all of these objectives for voltage restoration in droop-controlled inverter-based islanded micro-grids. By using distributed model predictive control (DMPC) in series with an unscented Kalman Filter (UKF), we design a secondary voltage controller to restore the voltage to the reference in finite time. The DMPC solves a reference tracking problem while rejecting reactive power disturbances in a noisy system. The method we present accounts for non-zero mean disturbances by design of a random-walk estimator. We validate the method's ability to restore the voltage in finite time via modeling a multi-node microgrid in Simulink.
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Award ID(s):
Publication Date:
Journal Name:
2019 IEEE 58th Conference on Decision and Control (CDC)
Page Range or eLocation-ID:
6242 to 6248
Sponsoring Org:
National Science Foundation
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