Modeling wind-turbine power curve: A data partitioning and mining approach
Modeling wind-turbine power curve: A data partitioning and mining approach
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DOI：
10.1016/j.renene.2016.10.032
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发表时间：
2017-03
期刊：
影响因子：
8.7
通讯作者：
Tinghui Ouyang;A. Kusiak;Yusen He
中科院分区：
文献类型：
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作者：
Tinghui Ouyang;A. Kusiak;Yusen He
功率曲线模型允许分析风力涡轮机的性能并将其与其他涡轮机进行比较。提出了一种基于数据划分中心和数据挖掘的方法来构造这种模型。风速范围被划分为计算中心的间隔。中心被视为建模中的代表性样本。采用支持向量机算法建立功率曲线模型。计算结果表明，该模型反映了功率曲线的动态特性。此外，它是准确和有效的生成。模型的准确性已与工业风能数据进行了测试。
Model of a power curve allows to analyze performance of a wind turbine and compare it with other turbines. An approach based on centers of data partitions and data mining is proposed to construct such a model. Wind speed range is partitioned into intervals for which centers are computed. The centers are regarded as representative samples in modeling. A support vector machine algorithm is used to build a power curve model. Computational results have demonstrated that the model reflects dynamic properties of a power curve. In addition it is accurate and efficient to generate. The model accuracy has been tested with industrial wind energy data.