• CN：11-2187/TH
• ISSN：0577-6686

• 可再生能源与工程热物理 •

### 基于云模型的风电机组输出功率特性分析

1. 1. 华北电力大学能源动力与机械工程学院 北京 102206;
2. 河南理工大学电气学院 焦作 454000
• 收稿日期:2016-11-23 修回日期:2017-04-26 出版日期:2017-11-20 发布日期:2017-11-20
• 通讯作者: 张光(通信作者),男,1992年出生。主要研究方向为风电机组性能分析、健康评估。E-mail:1142101385@qq.com
• 作者简介:董兴辉,男,1962年出生,博士,教授。主要研究方向为风电机组性能分析、健康评估和状态预警等。E-mail:1534506234@qq.com
• 基金资助:
河北省科技计划资助项目（15214370D）。

### Analysis of Wind Turbine Output Power Characteristic Based on Cloud Model

DONG Xinghui1, ZHANG Xinmiao1, ZHANG Guang1, WANG Shuai2

1. 1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206;
2. School of Electric Engineering, Henan Polytechnic University, Jiaozuo 454000
• Received:2016-11-23 Revised:2017-04-26 Online:2017-11-20 Published:2017-11-20

Abstract: The performance of the wind turbine has a direct impact on the safety production and economic benefit of the wind farm. The output power is one of the most important and representative performance indexes of the wind turbine. The wind power curve is the most direct expression of its electricity-generating capacity. Using the output power and wind speed as the data source, the characteristic value of cloud model is adopted to study the fluctuation characteristics of output power, which is beneficial to learning the production status of the wind turbine. Based on the data sieving of the wind speed and power data collected by wind turbine SCADA system, a scatter plot is drawn to describe the wind speed and power in the normal working state of the wind turbine, and the actual wind power curve of wind turbine is established by method of bins. The overall power cloud of different units can be obtained through the statistical analysis of the output power of different wind speed ranges and the output power cloud model of different wind speeds that is constructed by the reverse cloud generator. The output power size, the fluctuation range and the degree of dispersion are quantitatively analyzed through comparing the characteristic value of the power cloud. At the same time, the correlation coefficients of wind speed and power are calculated to reflect and evaluate the sensitivity of the wind turbine response. The application of cloud model allows the evaluation of unit state to develop from a qualitative one to a quantitative one, from a comprehensive macro assessment to a precise assessment based on different wind speed range segments. In this way, it improves the accuracy and comprehensiveness of wind turbine performance analysis. Lastly, an applied example is used to prove the effectiveness and reliability of the algorithm.