基于数据挖掘的配电网线路台风故障影响分级评价Grading Evaluation of Typhoon Fault Impact on Distribution Lines Based on Data Mining
孙明,苏毅方,邵先军,周金辉,金涌涛,陈诚
SUN Ming,SU Yifang,SHAO Xianjun,ZHOU Jinhui,JIN Yongtao,CHEN Cheng
摘要(Abstract):
台风灾害给电力系统造成巨大损失,严重威胁配电网可靠运行,因此,评估台风对配电网的影响至关重要。提出了一种基于大数据分析的配电网线路台风故障影响分级评价方法。首先,基于配电网在线监测大数据资源,构建配电网线路台风故障影响评价指标体系;其次,采用主成分分析和单层次分析等数据挖掘方法求取评价指标权重;然后,计算各条线路台风故障影响综合评分并划分影响等级。最后,选取中国某省的配电网台风故障线路数据进行算例分析,验证了所提算法的实用性和有效性。算法可精准定位配电网线路台风故障薄弱环节,对供电公司精准化运维和线路升级改造具有重要意义。
Typhoon disasters bring massive losses of the power system and serious threat to the reliability of distribution networks. It is very important to evaluate the impact of typhoon on distribution networks. This paper puts forward an intelligent grading evaluation method based on big data analysis for typhoon faults impact on distribution network. Firstly, based on the big data resources of distribution network on-line monitoring,the evaluation index system of typhoon fault impact on distribution network is constructed; secondly, the weight of evaluation index is calculated by data mining methods such as principal component analysis and single-level analysis method; thirdly, the comprehensive score of typhoon fault impact of each line is calculated and the grading level is divided. Finally, the data of typhoon fault lines of a provincial distribution network is analyzed to verify the practicability and effectiveness of the proposed method. The proposed method can accurately locate the weak links of typhoon fault lines in distribution network and is of great significance for power supply companies to implement precise operation and maintenance as well as lines transformation.
关键词(KeyWords):
配电网线路;数据挖掘;主成分分析;单层次分析;分级评价
distribution network lines;data mining;principal components analysis;single-level analysis;grading evaluation
基金项目(Foundation): 国网浙江省电力有限公司科技项目(5211DS18002R)
作者(Author):
孙明,苏毅方,邵先军,周金辉,金涌涛,陈诚
SUN Ming,SU Yifang,SHAO Xianjun,ZHOU Jinhui,JIN Yongtao,CHEN Cheng
DOI: 10.19585/j.zjdl.201910013
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- 配电网线路
- 数据挖掘
- 主成分分析
- 单层次分析
- 分级评价
distribution network lines - data mining
- principal components analysis
- single-level analysis
- grading evaluation