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Stratégies de commande distribuée pour l’optimisation de la production des fermes éoliennes

Abstract : In this PhD work we focus on the wind farm (WF) active power control since some of the new set grid requirements of interest can be expressed as specifications on its injection in the electric grid. Besides, one of our main objectives is related to the wind farm power maximization problem under the presence on non-negligible wake effect. The chosen WF control architecture has a two-layer hierarchical distributed structure. First of all, the wind turbine (WT) control is addressed. Here, a nonlinear controller lets a WT work in classic zones of functioning as well as track general deloaded power references. This last feature is a necessary condition to accomplish the WF control specifications. Secondly, the high level WF control problem is formulated as an optimization problem distributed among the WTs. Two novel distributed optimization algorithms are proposed, and their performance tested on different WF examples. Both are based on the well-known particle swarm optimization algorithm, which we modify and extend to be applicable in the multi-agent system framework. Finally, the overall WF control is evaluated by taking into account the WTs controlled dynamics. Simulations show potential significant power gains. Eventually, the introduction of a new control level in the hierarchical structure between the WF optimization and the WTs controllers is proposed. The idea is to let further cooperation among the WT local controllers, via a consensusbased technique, to enhance the overall system performance.
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Submitted on : Monday, June 11, 2018 - 10:51:25 AM
Last modification on : Wednesday, September 16, 2020 - 4:47:40 PM
Long-term archiving on: : Wednesday, September 26, 2018 - 5:14:22 PM


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  • HAL Id : tel-01812531, version 2


Nicolo Gionfra. Stratégies de commande distribuée pour l’optimisation de la production des fermes éoliennes. Autre. Université Paris-Saclay; La Sapienza, Università di Roma, 2018. Français. ⟨NNT : 2018SACLC017⟩. ⟨tel-01812531v2⟩



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