| 引用本文: | 朱倩雯,叶林,赵永宁,等.风电场输出功率异常数据识别与重构方法研究[J].电力系统保护与控制,2015,43(3):38-45.[点击复制] |
| ZHU Qianwen,YE Lin,ZHAO Yongning,et al.Methods for elimination and reconstruction of abnormal power data in wind farms[J].Power System Protection and Control,2015,43(3):38-45[点击复制] |
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| 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 |
| 关键词: 风电场 风电运行数据 电力大数据 异常数据 重构 |
| DOI：10.7667/j.issn.1674-3415.2015.03.006 |
| 投稿时间：2014-05-04 |
| 基金项目:国家自然科学基金项目(51477174，51077126) |
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| Methods for elimination and reconstruction of abnormal power data in wind farms |
| ZHU Qianwen,YE Lin,ZHAO Yongning,LANG Yansheng,SONG Xuri |
| (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;China Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| Electric power big data is an important resource for electric power development and comes from the procedures of electricity production and energy utilization. Wind power operating data is the major part of electric power big data. With the dramatic increase of wind power penetration, it is of great significance for wind farm operation, control and integration research by collection, processing and analysis of real historical operating data from wind farms. However, amounts of data collected from wind farms usually contain abnormal data, which have adverse impact on the study of fluctuation characteristics of wind power, wind power prediction, etc. The main source of abnormal data existed in wind farm historical operation data is analyzed and a model for eliminating abnormal data based on quartile method is presented. In the cases of missing data, methods based on patterns of similarity between neighboring wind farms outputs and multi-point cubic spline are used on the basis of historical data to reconstruct the discontinuous time series respectively. The case study indicates that the presented models are efficient for eliminating abnormal data and reconstructing missing data, which can be applied in practical engineering. |
| Key words: wind farm wind power operating data big data abnormal data reconstruction |
