摘要
针对传统基于统计的负荷曲线分类方法存在的准确性和低时效问题,将非侵入式负荷监测与分解技术拓展应用于变电站母线负荷曲线分解。考虑新能源出力,提出一种基于SVM和SCADA大数据的母线日净负荷曲线识别方法。首先,分析典型行业负荷有功功率曲线变化过程,提取有功突变时间进行负荷预筛选;然后,对有功功率波形进行傅里叶级数拟合,从而获取行业负荷特征标签,实现波形特征提取;其次,采用支持向量机将变电站母线日净负荷曲线波形特征分类识别,实现行业负荷特征辨识。最后对甘肃省电网某330 kV变电站实际数据进行SCADA仿真,结果表明,该方法可有效获取母线负荷类别,从而提升负荷建模效率。
In view of the accuracy and low time efficiency of the traditional load curve classification method based on statistics,the non-invasive load monitoring and decomposition technology is extended and applied to the bus load curve decomposition of substation in this paper.A method for identifying the daily net load curve of the bus based on SVM and SCADA big data is proposed by considering the output of new energy.Firstly,the change process of the load active power curve of typical industries is analyzed,and the active power mutation time for load pre-screening is extracted.Secondly,Fourier series are utilized to fit the active power waveform.The industry load feature tags are obtained,waveform features are extracted.In addition,for the purpose of identification of the industry load characteristics,the support vectors machine is employed for the waveform characteristics classification and recognization for the daily net load curve of the substation bus.In the end,a 330 kV substation in Gansu Power Grid is simulated by SCADA for verification.It is shown that this method can effectively classify the bus load,thereby improving the efficiency of load modeling.
作者
青灿
行舟
智勇
刘文飞
郝如海
马瑞
QING Can;XING Zhou;ZHI Yong;LIU Wenfei;HAO Ruhai;MA Rui(School of Electrical&Informational Engineering,Changsha University of Science&Technology,Changsha 410114,China;State Grid Gansu Electric Power Company,Lanzhou 730030,China;Electric Power Science Research Institute,State Grid Gansu Electric PowerCompany,Lanzhou 730070,China)
出处
《电力科学与技术学报》
CAS
北大核心
2022年第6期125-131,共7页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(51977012)
国家电网公司科技项目(52272218000X)。
