基于Copula理论的光伏功率高比例异常数据机器识别算法

GONG Yingfei

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基于Copula理论的光伏功率高比例异常数据机器识别算法

更新时间：2025-08-13

- 基于Copula理论的光伏功率高比例异常数据机器识别算法
- Automation of Electric Power Systems Vol. 40, Issue 9, Pages: 16-22(2016)
- 作者机构：
  
  电力系统及发电设备控制和仿真国家重点实验室, 清华大学,北京市,100084
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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GONG Yingfei. [J]. Automation of electric power systems, 2016, 40(9): 16-22.
DOI：

GONG Yingfei. [J]. Automation of electric power systems, 2016, 40(9): 16-22. DOI：

摘要

目前很多在运光伏电站由于通信故障、设备异常、人为限电等问题导致功率实测数据含高比例异常数据

极大阻碍了电站性能分析和功率数据的深化应用。基于Copula函数建立了描述辐照度与光伏功率间相关关系的概率功率曲线模型

进而针对光伏实测数据分散度、随机性强

异常数据比例高的特点

结合工程经验归纳了三类典型异常数据特征并提出了相应的异常数据机器识别模型。利用实测光伏电站数据和人工生成数据集进行仿真分析表明

采用该异常数据机器识别模型能适应高比例异常数据条件

有效识别各种类型异常数据

具有比常规3-sigma识别法更好的适应性和识别率。

Abstract

In many photovoltaic(PV)power plants

problems of communication errors

equipment failures and PV power curtailment result in high proportion of outliers in measured PV power data

which is difficult for performance analysis of PV power plants and application of power data. Hence

a new identification methodology is proposed. A probabilistic PV power curve model is proposed based on Copula theory to describe the relationship between the measured global radiation and PV power. Based on engineering experiences and the characteristics of high dispersion

strong randomness and a large proportion of outliers

machine identification models are proposed to identify three typical types of outliers. These methods are verified using measured data of PV power plants and artificial data. The effectiveness of applying the outlier identification methods is investigated through a day-ahead PV power forecasting application. Moreover

the proposed machine identification method is more adaptable and have higher accuracy than the conventional 3-sigma method. This work is supported by National Key Technologies R&D Program (No. 2013BAA01B03).

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