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Optimal control of wind farms for fatigue load minimization

机译:疲劳负荷最小化风电场的最优控制

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In this paper, we introduce a distributed control methodology that enables wind generators (WGs) to dynamically dispatch and regulate their power outputs to optimal equilibria in real-time. These equilibria are constructed such that the total mechanical fatigue loads experienced by WGs are minimized and a total assigned power demand is collectively met. We begin by posing the fatigue-load minimization constrained optimal control problem (FLMCOC) as a restricted agreement problem and then propose a fully distributed control methodology for recovering its solution that leverages a particular consensus+innovations algorithm. The distributed algorithm can be realized via any arbitrary peer-to-peer communication network and under any case, it can guarantee dynamic regulation of WGs' power outputs to their optimal values. Collectively, this paper offers a distributed control methodology for attaining a solution to the FLMCOC problem for large-scale wind farms that is computationally efficient, scalable, resilient to single-point communication or agent failures and privacy preserving. The proposed control methodology is validated through numerical simulations on the modified IEEE 24-bus power network.
机译:在本文中,我们介绍了一种分布式控制方法,使风力发电机(WGS)能够在实时地动态地调度和调节其功率输出到最佳均衡。构造这些均衡使得WGS经历的总机械疲劳负载最小化,共同满足总分配的电力需求。我们首先使疲劳负载最小化限制最佳控制问题(FLMCOC)作为限制的协议问题,然后提出了一种完全分布的控制方法,用于恢复其解决方案,该解决方案利用特定的共识+创新算法。分布式算法可以通过任何任意对等通信网络实现,并且在任何情况下都可以确保WGS电源输出的动态调节到其最佳值。本文共同提供了一种分布式控制方法,用于实现对大型风电场的FLMCOC问题的解决方案,该系统用于计算效率,可扩展,适应单点通信或代理故障和隐私保留。通过修改的IEEE 24总线电力网络上的数值模拟验证了所提出的控制方法。

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