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Dynamic modelling and non-linear control of TLP supported offshore wind turbine under environmental loads

Résumé : Offshore wind turbines (OWT) can be promising renewable energy devices. The motions of the turbine may be considerably high in severe sea-states and therefore they need to be operational after transition of such states. This work proposes a novel controller technique and its application in tension leg platform(TLP) supported OWT to harvest optimized power. It is achieved using the nonlinear quadratic regulator based algorithm wherein the state dependent structural system matrices are modified appropriately to account for the fluctuating dynamics. Three wind speeds (15 m/s, 21 m/s and 25 m/s) along with turbulence intensity of 0.1 are selected which are above the rated wind speed as the controller mechanism is effective for those regions. Since the wind and wave loads are taken as random, Monte Carlo method is used for the analysis to rule out epistemic uncertainty by ensemble statistics. The results show that the proposed nonlinear quadratic regulator is able to control the power, generator torque and rotor speed effectively without additional increase in platform motions vis-á-vis existing conventional Baseline controller. It is also observed that there are lesser fluctuations using the proposed controller compared to existing controller. The ensemble statistics –maxima and mean— are close to the reference value when wind speeds are close to rated-wind speed; whereas the platform motions and tower base forces is less using the proposed NQR controller or at least similar to the Baseline controller. The ensemble standard deviation are far less which shows the output power is controlled effectively.
Domaine de référence : EMR
Auteur Manikandan R., Saha Nilanjan
Année de parution : 2019.
Mots-clés : Nonlinear control and dynamics, Offshore wind turbines, Quadratic regulator, Response statistics, Tension leg platform, Wind and wave loads.
Type de document : Article de revue.
Exporter la référence : BibTeX | Zotero RDF | RIS (EndNote)

Ce projet est financé par le Fonds Européen de Développement Régional, la Région Normandie et le Conseil Départemental de la Manche.