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Coordinated Control for Optimal Wind Farm Operation

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Abstract:
Typical wind farm behaves as an uncoordinated collection of wind turbines (i.e., as a source of significant stochastic disturbances in the power system), with each turbine independently controlled to maximize its local power production. However, to ensure full integration of wind farms in the electric power system, as demanded by the grid codes, they should respond to the requirements of the grid operator similarly to the classic power plants. To that end, we describe an optimization-based coordinated strategy for wind farm control. The objectives for the farm are to produce demanded power and to minimize the loads on mechanical parts of wind turbines that occur due to turbulent winds. The load reduction is achieved by dynamic adaptation of individual wind turbine power references to the current (turbulence induced) disturbances. The changes in operation of all wind turbines are coordinated to ensure that the wind farm produces power according to the grid operator demands. The pursued approach uses parametric programming to find the optimal control actions for the wind farm with respect to the constraints inherent to the system. The considered control design is also motivated by the need for efficient controller computation that will allow implementations on large wind farms.

Type of Seminar:
IfA Seminar
Speaker:
Prof. Mato Baotić
Department of Control and Computer Engineering, University of Zagreb
Date/Time:
Mar 20, 2012   15:15
Location:

HG D 5.2, Rämistr. 101
Contact Person:

Manfred Morari
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Biographical Sketch:
Mato Baotić received the B.Sc. and M.Sc. degrees, both in Electrical Engineering, from the Faculty of Electrical Engineering and Computing (FER Zagreb), University of Zagreb, Croatia, in 1997 and 2000, respectively. In 2005 he received the PhD from the Automatic Control Laboratory at ETH Zurich, Switzerland. He is currently an Associate Professor at the Department of Control and Computer Engineering, FER Zagreb, Croatia. His research interests include mathematical programming, hybrid systems, model predictive control and application of optimal control in renewable energy systems.