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基于 Thompson tau-四分位和多点插值的风功率异常数据处理
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1.湖南大学电气与信息工程学院;2.贵州电网有限责任公司电力科学研究院

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国家自然科学基金项目(面上项目,重点项目,重大项目),贵州电网公司科技项目(GZKJXM20171048)


Processing of Wind Power Abnormal Data Based on Thompson tau-quartile and Multi-point Interpolation
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Electric Power Research Institute of Guizhou Power Grid Co., Ltd.

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    摘要:

    针对传统风电机组风速-功率异常运行数据处理清洗时间长、模型复杂的问题,提出一种基于Thompson tau-四分位法和多点插值的异常数据处理算法。首先,对切入风速与切出风速间等于或小于异常功率数据点予以剔除,通过划分风速区间,采用Thompson tau-四分位法对异常运行数据进行分段精细化清洗,降低异常点辨识的模型复杂度;然后,通过四点插值细分算法对清洗后的异常数据进行重构,获得完整的风速-功率有效数据;最后,以风电机组实际风速-功率数据为算例进行对比分析,结果表明本文提出的清洗方法实现简单、效率更高,尤其在临近风电场数据缺失、异常、不可用情况下,本文提出的重构方法能有效提高重构数据质量,且重构效果更优。

    Abstract:

    An abnormal data processing algorithm based on Thompson tau-quartile method and multi-point interpolation is proposed to solve the problems of long cleaning time and complex model in the processing of wind speed-active power abnormal operation data of traditional wind turbine. Firstly, the abnormal power data points between the cut-in wind speed and the cut-out wind speed that are equal to or less than zero are eliminated. By dividing the wind speed interval, the Thompson tau-quartile method is used to segment and refine the abnormal operation data to reduce the complexity of the model for identifying abnormal points. Then, the cleaned abnormal data are reconstructed by four-point interpolation subdivision algorithm to obtain the complete effective wind speed-active power data. Finally, the actual wind speed-active power data of the wind turbine are used as an example for the comparative analysis. The results show that the proposed method is simpler and the cleaning efficiency is higher. In the case of data missing, abnormal and unavailable in the adjacent wind farm, the proposed reconstruction method can effectively improve the quality of reconstructed data and get better reconstruction results. This work is supported by National Natural Science Foundation of China (No. 51777061) and Guizhou Power Grid Co. Ltd.(No. GZKJXM20171048).

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引用本文

邹同华,高云鹏,伊慧娟,等.基于 Thompson tau-四分位和多点插值的风功率异常数据处理[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20191231003.
ZOU Tonghua,GAO Yunpeng,YI Huijuan,et al.Processing of Wind Power Abnormal Data Based on Thompson tau-quartile and Multi-point Interpolation[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191231003.

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  • 收稿日期:2019-12-31
  • 最后修改日期:2020-05-19
  • 录用日期:2020-05-06
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