Adaptive Extremum Seeking Control of Urban Area Wind Turbines

Felix Dietrich, Steffen Borchers-Tigasson, Till Naumann and Horst Schulte
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Felix Dietrich: Automation and Intelligent Systems Group/Control Engineering Group, Faculty 1: School of Engineering—Energy and Information, University of Applied Sciences (HTW) Berlin, 12459 Berlin, Germany
Steffen Borchers-Tigasson: Automation and Intelligent Systems Group/Control Engineering Group, Faculty 1: School of Engineering—Energy and Information, University of Applied Sciences (HTW) Berlin, 12459 Berlin, Germany
Till Naumann: MOWEA—Modulare Windenergieanlagen GmbH, Storkower Str. 115A, 10407 Berlin, Germany
Horst Schulte: Automation and Intelligent Systems Group/Control Engineering Group, Faculty 1: School of Engineering—Energy and Information, University of Applied Sciences (HTW) Berlin, 12459 Berlin, Germany

Energies, 2021, vol. 14, issue 5, 1-12

Abstract: Maximum-power point tracking of wind turbines is a challenging issue considering fast changing wind conditions of urban areas. For this purpose, an adaptive control approach that is fast and robust is required. Conventional approaches based on simple step perturbations and subsequent observation, however, are difficult to design and too slow for the demanding wind conditions of urban areas including gusts and turbulence. In this paper, an extremum seeking control scheme to the recently developed wind turbine MOWEA (Modulare Windenergieanlagen GmbH) is proposed and successfully applied. To this end, a comprehensive aero-electromechanical model of the wind turbine under study including basic control is formulated. Next, the extremum seeking control scheme is adapted to the system. Several aspects to increase adaptation speed are highlighted, including a novel phase compensation. Finally, a validation of the proposed approach is performed considering real wind data, thus demonstrating its fast and robust adaptability. The proposed control scheme is computationally efficient and can be easily implemented on the existing onboard electronics.

Keywords: adaptive control; control of renewable energy resources; extremum seeking control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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