[1]刘 娟,鲁丽萍,鞠登峰,等.基于灰色关联分析算法的变压器预警研究[J].计算机技术与发展,2022,32(10):215-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 035]
　LIU Juan,LU Li-ping,JU Deng-feng,et al.Research on Transformer Early Warning Based on Grey Relational Analysis Algorithm[J].,2022,32(10):215-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 035]
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基于灰色关联分析算法的变压器预警研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
215-220
栏目:
新型计算应用系统
出版日期:
2022-10-10

文章信息/Info

Title:
Research on Transformer Early Warning Based on Grey Relational Analysis Algorithm
文章编号:
1673-629X(2022)10-0215-06
作者:
刘 娟1 ; 鲁丽萍1 ; 鞠登峰1 ; 邹丹平1 ; 张 鹏2 ; 邵梦雨2 ; 齐 波2
1. 国网电力科学研究院有限公司,北京 100193;
2. 新能源电力系统国家重点实验室(华北电力大学),北京 102206
Author(s):
LIU Juan1 ; LU Li-ping1 ; JU Deng-feng1 ; ZOU Dan-ping1 ; ZHANG Peng2 ; SHAO Meng-yu2 ; QI Bo2
1. State Grid Electric Power Research Institute,Beijing 100193,China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources( North China Electric Power University) ,Beijing 102206,China
关键词:
变压器; 关联规则; 关联关系; 灰色关联分析; 变压器预警
Keywords:
transformer; association rules; association relationship; grey correlation analysis; transformer warning
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 035
摘要:
变压器是电力系统中的重要枢纽设备,其运行状态直接关系电力系统的安全稳定。 目前,对变压器的预警主要依赖状态量的阈值比较法实现,对于异常数据较为敏感,预警准确率较低。 为了解决上述问题,该文利用灰色关联分析方法在多状态量关联关系挖掘方面的优势对变压器状态量进行分析,提出了基于灰色关联分析算法的变压器预警方法。 首先,对能够反映变压器运行状态的关键状态量进行梳理;之后,利用传统导则对关键状态量进行初步研判,利用灰色关联分析计算状态量之间的关联度,并基于关联度进行排序;根据状态量的异常情况,确定需要重点关注的状态量,以及与该异常状态量密切关联的其他状态量;最后,根据状态量的关联度变化趋势确定缺陷或故障类型。 现场的实例表明,该方法从状态量监测数据中挖掘关联关系变化规律,实现了对变压器缺陷的准确预警,预警准确率可达 90. 00% 。 基于灰色关联规则挖掘的变压器预警方法解决了传统预警方法准确率低的问题,可为现场运维检修工作提供有力支撑。
Abstract:
Transformer is an important hub device in the transmission system,and its operating status is directly related to the safety and stability of? the power system. At present,the early warning of transformers mainly relies on the threshold comparison method of state variables,which? is more sensitive to abnormal data. The early warning accuracy rate is low. In order to solve the above problem,we analyze the transformer state quantity by using the advantage of grey correlation analysis method in mining the association relation of multi-statequantity,and put forward a transformer early warning method based on grey correlation analysis algorithm. Firstly, the critical state variables that can reflect the operating state of the transformer are sorted out. Secondly,the traditional guide method is used to make preliminary judgments on the critical state variables,and the gray correlation analysis method is used to calculate the correlation between the state variables. Then, the state variables are sorted based on the degree of relevance. According to the abnormal situation, the state variable to be paid attention to and the state variables closely related to the abnormal state variable are determined. Finally,the defect or failure of the transformer is determined according to the change of the state variables relevance degree. Actual examples show that the proposed method mines the relationship change the law from the state variable monitoring data,and realizes accurate early warning of transformer defects,and the early warning accuracy rate can reach 90. 00% . The transformer early warning method based on gray association rule mining solves the low accuracy of traditional early warning,which can provide strong support for on-site operation and maintenance work.

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更新日期/Last Update: 2022-10-10