Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint

Kathryn Johnson, Christopher Bay, Lucy Pao, Timothy Taylor

Research output: Contribution to conferencePaper

Abstract

Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.
Original languageAmerican English
Number of pages8
StatePublished - 2018
EventAmerican Control Conference - Milwaukee, Wisconsin
Duration: 27 Jun 201829 Jun 2018

Conference

ConferenceAmerican Control Conference
CityMilwaukee, Wisconsin
Period27/06/1829/06/18

NREL Publication Number

  • NREL/CP-5000-70936

Keywords

  • distributed control
  • optimization
  • wind farm control

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