摘要
针对风电机组性能分析过程繁琐低效、数据清洗不彻底以及传统方法难以有效识别复杂多变的异常发电状态的问题,提出一种用于风电机组功率曲线分析的数据清洗算法。通过分析风电机组数据采集与监控(SCADA)系统采集的风速功率数据,优化数据处理规则与数据分析过程,提出最优组内方差清洗算法,检测机组发电性能异常的状态,降低对检测工具和数据维度的硬性要求。实例分析表明该方法实用、高效,在不增加硬件设备投资的前提下,能准确清洗风电机组功率曲线数据并识别出机组异常运行状态,显著提高了风电机组性能分析的准确性。
In view of inefficient analysis process for wind turbine performance,inaccurate data cleaning and hard to identifying of wind power generation status,a data cleaning algorithm is put forward to analyze the wind turbines power curve.Through the analysis of the wind power curve data collected by supervisory control and data acquisition（SCADA）system,the optimal interclass variance algorithm is proposed to identify the poor performance status accurately with optimizing data processing rules and analysis approach.The detection tools and data dimension are not the obstacles of power curve analysis.Example analysis shows that the method is practical and efficient,and can accurately clean wind turbines power curve data and identify abnormal status performance under the premise of no increase in the hardware equipment investment,while significantly improving the wind turbines performance analysis accuracy.
出处
《电力系统自动化》
EI
CSCD
北大核心
2016年第10期116-121,共6页
Automation of Electric Power Systems
基金
吉林省科技发展计划资助项目(20150204084GX)~~
关键词
风电机组
功率曲线
数据处理
状态检测
wind turbine
power curve
data processing
status detection
作者简介
娄建楼（1972-）。男，副教授，主要研究方向：电力大数据处理。E-mail：loujianlou@nedu．edu．cn
胥佳（1985-），男，高级工程师．主要研究方向：风力发电。E-mail：xujia@clypg．com．cn
陆恒（1990-）．男，通信作者，硕士研究生．主要研究方向：数据挖掘与数据可视化。E-mail：luheng212@126．com
