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。文中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。
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 abnormal data and missing values. In this paper, a load preprocessing model based on fuzzy clustering and grey relational analysis （GRA-FCM） 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.
出处
《电测与仪表》
北大核心
2017年第11期36-42,共7页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(51207988)
上海市科委科创项目(14DZ1201602)
国家电网公司科技项目(SGRI-DL-71-14-004
52094014001Z)
上海绿色能源并网工程技术研究中心(13DZ2251900)
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
load preprocessing, grey relational
analysis, fuzzy clustering analysis, similar sample set, typical load profile
