风电场输出功率异常数据识别与重构方法研究

发布时间：2019-04-16 20:18

【摘要】：电力大数据是电力发展的重要资源,数据来源于电力生产和电能使用的各个环节。风电运行数据是电力大数据的重要组成部分,随着风电穿透功率的增大,风电数据的采集、处理、分析对风电场运行、控制与并网研究有重要意义。然而,从风电场收集到的大量数据中通常包含异常数据点,这样的异常点给风电功率波动特性、风电功率预测等方面研究带来负面影响。分析了风电场历史运行数据中存在的异常数据的主要来源,并针对该实际问题,采用基于四分位算法的数学模型对异常数据进行识别。在数据缺失的情况下,以可用历史数据为基础,采用基于临近风电场出力模式性的方法和多点三次样条插值方法重构出完整的时间序列。算例分析给出了两种重构方法的重构效果以及各自的适应性,结果表明采用所提出的方法能够有效识别、剔除异常数据并重构缺失数据,对不同风电场有较强的通用性,具有一定的工程实用价值。
[Abstract]:Power big data is an important resource for the development of electric power, and the data comes from every link of electric power production and power use. Wind power operation data is an important part of power big data, with the increase of wind power penetration power, wind power data collection, processing, analysis of wind farm operation, control and grid-connected research is of great significance. However, a large number of data collected from wind farms usually contain abnormal data points, which have a negative impact on wind power fluctuation characteristics, wind power prediction and other aspects. The main sources of abnormal data in historical operation data of wind farm are analyzed, and the mathematical model based on quartile algorithm is used to identify the abnormal data in view of this practical problem. In the case of lack of data, based on the available historical data, the complete time series is reconstructed by using the method based on the output mode of the nearby wind farm and the multi-point cubic spline interpolation method. The results show that the proposed method can effectively identify, eliminate abnormal data and reconstruct missing data, which has strong generality to different wind farms, and the results show that the proposed method can effectively identify the abnormal data and reconstruct the missing data, and the results show that the proposed method can effectively identify and eliminate the abnormal data and reconstruct the missing data. It has certain engineering practical value.
【作者单位】：中国农业大学信息与电气工程学院;中国电力科学研究院;
【基金】：国家自然科学基金项目(51477174,51077126)~~
【分类号】：TM614

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