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arXiv 1503.06456eess.SYcs.SYmath.OC
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用于减少风场机械疲劳的模型预测控制器

Model predictive controllers for reduction of mechanical fatigue in wind farms

United Technologies Research Center Ireland(联合技术研究院爱尔兰);Università degli Studi di Pavia(帕维亚大学)

Stefano Riverso, Simone Mancini, Fabio Sarzo, Giancarlo Ferrari-Trecate

AI总结:

本文提出基于MPC的风场功率调度算法,通过引入风湍流ARMA预测器降低不确定性,实现机械应力的减少,验证了在大型风场中的有效性。

摘要:

我们考虑了将风场(WF)的功率需求调度到单个风力涡轮机(WT)以最小化机械应力的问题。我们假设风力足够强,使每个WT能够产生所需功率,并提出了不同的闭环模型预测控制(MPC)调度算法。与现有基于MPC的方法类似,我们的方法不需要改变WT硬件,只需对风场的SCADA系统进行软件更改。然而,与之前的MPC方案不同,我们通过引入风湍流的ARMA预测器来增强WT模型,从而减少MPC控制周期内的风力预测不确定性。这使得我们能够开发出随机和确定性MPC算法。为了比较不同的MPC方案并展示与经典开环调度器相比的改进,我们使用MatLab的SimWindFarm工具箱进行了模拟。我们证明MPC控制器即使在大型风场如100台WT的Thanet海上风场中,也能实现应力的减少。

英文摘要:

We consider the problem of dispatching WindFarm (WF) power demand to individual Wind Turbines (WT) with the goal of minimizing mechanical stresses. We assume wind is strong enough to let each WTs to produce the required power and propose different closed-loop Model Predictive Control (MPC) dispatching algorithms. Similarly to existing approaches based on MPC, our methods do not require changes in WT hardware but only software changes in the SCADA system of the WF. However, differently from previous MPC schemes, we augment the model of a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty in wind predictions over the MPC control horizon. This allows us to develop both stochastic and deterministic MPC algorithms. In order to compare different MPC schemes and demonstrate improvements with respect to classic open-loop schedulers, we performed simulations using the SimWindFarm toolbox for MatLab. We demonstrate that MPC controllers allow to achieve reduction of stresses even in the case of large installations such as the 100-WTs Thanet offshore WF.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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