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A Frequency and Duration Method for Adequacy Assessment of Generation Systems With Wind Farms

机译:风电场发电系统充分性评估的频率和持续时间方法

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摘要

This paper proposes a frequency and duration method for the adequacy assessment of generation systems with wind farms (WFs). Based on Markov chains, reliability models for WFs, conventional generators, and load are developed and represented as the corresponding probability-frequency distribution functions (PFDFs). The failures of wind turbines and the uncertainties and correlations of wind speeds are considered and incorporated into the reliability modeling of WFs. With the proposed method, the implementation of the reliability modeling of large WFs becomes simple. Moreover, for a generation system with WFs, not only probability-based reliability indexes, such as the loss of load probability and the expected energy not supplied, but also frequency-based reliability indexes, such as the loss of load frequency and the loss of load duration, can be calculated analytically and efficiently using the PFDFs. An optimal decision-making model for determining the clustering number of random states is proposed to improve the credibility of stochastic process modeling of Markov-chain-based random variables. The tests on two modified IEEE-RTS79 grids with WFs demonstrate the rapidity and validity of the proposed method.
机译:本文提出了一种频率和持续时间方法,用于评估带有风电场的发电系统(WF)。基于马尔可夫链,开发了WF,常规发电机和负载的可靠性模型,并将其表示为相应的概率频率分布函数(PFDF)。考虑了风力涡轮机的故障以及风速的不确定性和相关性,并将其纳入WFs的可靠性模型中。使用所提出的方法,大型WF的可靠性模型的实现变得简单。此外,对于具有WF的发电系统,不仅基于概率的可靠性指标(如未提供负载概率和预期能量的损失),而且基于频率的可靠性指标(如负载频率的损失和负载的损失)。使用PFDF可以分析有效地计算出载荷持续时间。提出了一种确定随机状态聚类数量的最优决策模型,以提高基于马尔可夫链的随机变量随机过程建模的可信度。在两个带有WF的修改的IEEE-RTS79网格上的测试证明了该方法的快速性和有效性。

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