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文章摘要
基于灰色关联与模糊聚类分析的负荷预处理方法
Load Preprocessing Method Based on Grey Relational Analysis and Fuzzy Clustering
Received:July 31, 2016  Revised:September 11, 2016
DOI:
中文关键词: 负荷预处理  灰色关联分析  模糊聚类分析  相似样本集  典型特征曲线
英文关键词: Grey  Relational Analysis, Fuzzy  Clustering, Similar  Sample Set, Typical  Load Profile
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)上海市科委科创项目,国家电网公司科技项目
Author NameAffiliationE-mail
Lin Shunfu* College of Electrical Engineering,Shanghai University of Electric Power xiechao0908@163.com 
Xie Chao College of Electrical Engineering,Shanghai University of Electric Power xiechao0908@163.com 
Li Dongdong College of Electrical Engineering,Shanghai University of Electric Power xiechao0908@163.com 
Fu Yang College of Electrical Engineering,Shanghai University of Electric Power xiechao0908@163.com 
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中文摘要:
      电力负荷曲线反映了在一定时间间隔内用户侧消耗的电能,包含了电力系统运行调度与可靠性等重要信息。然而信道错误、仪表故障、设备停运等随机因素导致负荷曲线包含异常数据与缺失值。提出一种基于灰色关联分析和模糊聚类(GRA-FCM)的负荷预处理模型。首先通过灰色关联分析确定与待检测日关联度较大的相似样本集,然后采用模糊聚类算法与聚类有效指标得到典型特征曲线,最后对辨识的异常数据进行修正。将所提模型应用于某城市电网SCADA系统负荷预处理中,表明所提模型有很高的准确性和实用性。
英文摘要:
      Load profiles reflect electric energy consumption of consumers, including the information of day-to-day operations and system reliability. However, some random factors such as channel errors, unexpected interruption or shutdown of power stations can result in load profiles contain outliers and missing values. In this paper, a data preprocessing model based on fuzzy clustering and grey relational analysis is proposed. Firstly, the similar sample set with larger correlation degree is determined by grey correlation analysis. Then the typical load profiles are obtained by adopting fuzzy clustering algorithm and clustering validity index. Finally, the correction is performed on the abnormal data of identification. The proposed model is applied to a city grid SCADA system, which proves the model has high accuracy and practicability.
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