基于自适应DBSCAN算法的开关柜绝缘状态评价方法

doi:10.19595/j.cnki.1000-6753.tces.L90024

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Abstract

In order to evaluate the insulation state of switchgear more reasonably and explore the value of abnormal data, an evaluation method based on adaptive DBSCAN (density based spatial clustering of applications with noise) is proposed. Firstly, the deviation of insulation state detection data transient to ground voltage and ultrasonic wave is calculated to eliminate the background noise; the deviation of insulation state, the ambient temperature, humidity and operation life of the switchgear constitute the characteristic quantity of insulation state of switchgear, and the multi-dimensional characteristic dataset of switchgear is established. Then, K-means nearest neighbor method and mathematical expectation method are used to generate scanning radius and minimum number candidate parameters of DBSCAN algorithm, and density threshold is introduced to automatically find the sTab.range of cluster number; parameters in DBSCAN algorithm are selected adaptively by using distribution characteristics of dataset to avoid the error caused by subjective setting. Finally, the cluster validity index based on weighted ensemble is constructed to judge the validity of clustering results. The field test data is used as an example by comparing the results of K-means and DBSCAN to verify the feasibility, which provides a theory for the evaluation of the operation state of the switchgear basis.

Key words： Switchgear state evaluation adaptive optimization DBSCAN algorithm outlier effectiveness index

Received: 11 May 2020

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TM591

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	Hu Jinlei
	Lai Junju
	Li Yangyang
	Yang Fan
	Huang He

Cite this article:

Hu Jinlei,Lai Junju,Li Yangyang等. Evaluation Method of Switchgear Insulation State Based on Adaptive Density Based Spatial Clustering of Applications with Noise Algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(zk1): 344-352.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90024 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/Izk1/344

[1] 蒲天骄, 乔骥, 韩笑, 等. 人工智能技术在电力设备运维检修中的研究及应用[J]. 高电压技术, 2020, 46(2): 369-383.
Pu Tianjiao, Qiao Ji, Han Xiao, et al.Research and application of artificial intelligence in operation and maintenance for power equipment[J]. High Voltage Engineering, 2020, 46(2): 369-383.
[2] 张燕, 方瑞明. 基于油中溶解气体动态网络标志物模型的变压器缺陷预警与辨识[J]. 电工技术学报, 2020, 35(9): 2032-2041.
Zhang Yan, Fang Ruiming.Fault detection and identi-fication of transformer based on dynamical network marker model of dissolved gas in oil[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2032-2041.
[3] 谢静, 束洪春, 王科. 基于模糊分层理论的高压开关柜状态评价算法[J]. 高电压技术, 2014, 40(10): 3192-3196.
Xie Jing, Shu Hongchun, Wang Ke.State evaluation based on AHP fuzzy theory of high voltage switch- gears[J]. High Voltage Engineering, 2014, 40(10): 3192-3196.
[4] 贾亚楠, 刘东明, 随慧斌. 基于正态云模型和D-S证据理论的开关柜运行状态综合评估[J]. 高压电器, 2017, 53(9): 247-252.
Jia Yanan, Liu Dongming, Sui Huibin.Comprehen- sive evaluation for switchgear based on the normal cloud model and D-S evidence theory[J]. High Voltage Apparatus, 2017, 53(9): 247-252.
[5] 陈曦, 唐斌, 余飞. 基于变权重理论和融合实时信息配电开关柜的状态评价[J]. 电子测量与仪器学报, 2018, 32(8): 193-200.
Chen Xi, Tang Bin, Yu Fei.State evaluation of distribution switchboard based on variable weight theory and fusion of real time information[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(8): 193-200.
[6] 谢静, 束洪春, 王科, 等. 基于FCM算法的高压开关柜局部放电状态评价方法研究[J]. 高压电器, 2015, 51(10): 82-90, 96.
Xie Jing, Shu Hongchun, Wang Ke, et al.Study on state evaluation of partial discharge in high voltage switchgear based on fuzzy clustering algorithm[J]. High Voltage Apparatus, 2015, 51(10): 82-90, 96.
[7] 滕予非, 吴杰, 张真源, 等. 基于离群点检测的高压并联电抗器本体电流互感器测量异常故障在线诊断[J]. 电工技术学报, 2019, 34(11): 2405-2414.
Teng Yufei, Wu Jie, Zhang Zhenyuan, et al.Online identification of measurement abnormality fault based on outlier detection for current transformer in high voltage shunt reactor[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2405-2414.
[8] 亢超群. 基于多源信息融合的配电开关柜状态评价方法研究[D]. 北京: 华北电力大学, 2016.
[9] 赵仕策, 赵洪山, 寿佩瑶. 智能电力设备关键技术及运维探讨[J]. 电力系统自动化, 2020, 44(20): 1-11.
Zhao Shice, Zhao Hongshan, Shou Peiyao.Dis- cussion on key technology and operation & maintenance of intelligent power equipment[J]. Auto- mation of Electric Power Systems, 2020, 44(20): 1-11.
[10] 邹阳, 何倩玲, 蔡金锭. 基于组合赋权-双基点法的变压器油纸绝缘状态综合评估[J]. 电工技术学报, 2019, 34(20): 4400-4408.
Zou Yang, He Qianling, Cai Jinding.Comprehensive evaluation of transformer oil-paper state based on combined weight-double base point method[J]. Transactions of China Electrotechnical Society, 2019, 34(20): 4400-4408.
[11] 罗伟明, 吴帆, 黄业广, 等. 基于局部密度聚类算法的变压器故障状态评估[J]. 广东电力, 2018, 31(8): 81-90.
Luo Weiming, Wu Fan, Huang Yeguang, et al.Transformer fault state evaluation method based on multilocal density clustering algorithm[J]. Guangdong Electric Power, 2018, 31(8): 81-90.
[12] 张为金. 基于机器学习的电力异常数据检测[D]. 成都: 电子科技大学, 2018.
[13] 李春燕, 蔡文悦, 赵溶生, 等. 基于优化SAX和带权负荷特性指标的AP聚类用户用电行为分析[J]. 电工技术学报, 2019, 34(增刊1): 368-377.
Li Chunyan, Cai Wenyue, Zhao Rongsheng, et al.Customer behavior analysis based on affinity propagation algorithm with optimized SAX and weighted load characteristic indices[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 368-377.
[14] 徐搏超. 基于参数关联性的电站参数异常点清洗方法[J]. 电力系统自动化, 2020, 44(20): 142-149.
Xu Bochao.Parameter correlation based parameter abnormal point cleaning method for power station[J]. Automation of Electric Power systems, 2020, 44(20): 142-149.
[15] 毋雪雁, 王水花, 张煜东. K最近邻算法理论与应用综述[J]. 计算机工程与应用, 2017, 53(21): 1-7.
Wu Xueyan, Wang Shuihua, Zhang Yudong.Survey on theory and application of K-nearest-neighbors algorithm[J]. Computer Engineering and Applications, 2017, 53(21): 1-7.
[16] 周国亮, 宋亚奇, 王桂兰, 等. 状态监测大数据存储及聚类划分研究[J]. 电工技术学报, 2013, 28(增刊2): 337-344.
Zhou Guoliang, Song Yaqi, Wang Guilan, et al.Research of condition monitoring big data storage and clustering[J]. Transactions of China Electro- technical Society, 2013, 28(S2): 337-344.
[17] 董骁翀, 孙英云, 蒲天骄, 等. 一种基于Wasserstein距离及有效性指标的最优场景约简方法[J]. 中国电机工程学报, 2019, 39(16): 4650-4658, 4968.
Dong Xiaochong, Sun Yingyun, Pu Tianjiao, et al.An optimal scenario reduction method based on Wasserstein distance and validity index[J]. Pro- ceedings of the CSEE, 2019, 39(16): 4650-4658, 4968.
[18] Liang Shaoyi, Han Deqiang, Yang Yi.Cluster validity index for irregular clustering results[J]. Applied Soft Computing, 2020, 95: 106583.
[19] 马克勤, 杨延娇, 秦红武, 等. 结合最大最小距离和加权密度的K-means聚类算法[J]. 计算机工程与应用, 2020, 56(16): 50-54.
Ma Keqin, Yang Yanjiao, Qin Hongwu, et al.K-means clustering algorithm combining max-min distance and weighted density[J]. Computer Engin- eering and Applications, 2020, 56(16): 50-54.
[20] 田力, 向敏. 基于密度聚类技术的电力系统用电量异常分析算法[J]. 电力系统自动化, 2017, 41(5): 64-70.
Tian Li, Xiang Min.Abnormal power consumption analysis based on density-based spatial clustering of applications with noise in power systems[J]. Auto- mation of Electric Power Systems, 2017, 41(5): 64-70.