摘要
风电场功率数据中包含大量异常数据,难以反映风电场真实的风能情况,会影响风电功率预测的精度,从而影响电网决策。针对该问题,通过分析风电场异常数据特征,将其分为堆积型和分散型,并基于时间序列变点检测理论,将密度比是否为恒值作为剔除堆积型异常数据的判断准则,采用改进Kullback Leibler重要性估计程序(improved Kullback Leibler importance estimation program,IKLIEP)剔除堆积型异常数据;再采用四分位法剔除分散型异常数据。最后将所提方法应用于国内蒙西某130.5 MW的风电场,实验结果表明所提方法能够更有效地识别并剔除异常数据,平均识别率提高了6.19%,误识别率降低了2.92%,验证了所提方法的有效性。
The wind farm power data contain a large number of abnormal data,which are difficult to reflect the real wind energy situation of the wind farm,affecting the accuracy of wind power forecasting,thereby affecting the decision-making of the power grid.In order to solve this problem,we analyzed the characteristics of abnormal data of wind farms,and divided them into accumulation type and scattered type.Based on the time series change point detection theory,whether the density ratio is a constant value was used as the judgment criterion for eliminating accumulation type abnormal data.The improved Kullback Leibler importance estimation program(IKLIEP)was used to eliminate the stacked abnormal data,and the quartile method was used to eliminate the scattered abnormal data.Finally,the method in this paper was applied to a 130.5 MW wind farm in western Mongolia.The experimental results show that the method in this paper can ne adopted to more effectively identify and eliminate abnormal data,the average recognition rate is increased by 6.19%,and the false recognition rate is reduced by 2.92%,which proves the verifying the effectiveness of the method.
作者
杨茂
张书天
王天硕
杨硕
赵辉
YANG Mao;ZHANG Shutian;WANG Tianshuo;YANG Shuo;ZHAO Hui(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education,Northeast Electric Power University,Jilin 132012,China;Taiping Bay Power Plant,Lüyuan Hydropower Company,Northeast Branch of State Grid,Dandong 118000,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2023年第7期2952-2960,共9页
High Voltage Engineering
基金
国家重点研发计划(大规模风电/光伏多时间尺度供电能力预测技术)(2022YFB2403000)。
作者简介
通信作者:杨茂1982—,男,博士,教授,博导主要研究方向为电力系统稳定分析与新能源并网,E-mail:yangmao820@163.com。
