考虑多元因素态势演变的配电变压器迁移学习故障诊断模型
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摘要
针对配电变压器故障数据有限及数据过期的问题,提出一种考虑多元因素态势演变的配电变压器迁移学习故障诊断模型。首先,构建了配变运行状态评价指标体系,对指标状态量进行模糊二元量化,利用模糊Apriori算法挖掘其与故障之间的关联关系,提取诱导变压器故障的关键状态量。针对配变故障数据有限,引入Tanimoto系数,将有效的辅助故障数据迁移至目标配变,建立了基于信息迁移的配变故障诊断模型;针对配变故障数据过期,引入健康指数描述配变状态,将不同健康等级的辅助故障数据进行迁移,建立了针对数据过期的配变故障诊断模型。在此基础上,利用迁移学习算法TrAdaBoost对上述模型中目标与辅助故障数据的权重进行迭代求解,进而输出配变故障强诊断器。最后,根据配变故障数据进行算例分析,仿真结果表明,该文所建模型故障诊断精度高,具有比传统诊断器更强的泛化能力。
Aiming at the problem of limited fault data and data expiration of distribution transformers, a transfer learning fault diagnosis model of distribution transformer considering multifactor situation evolution is proposed in this paper. Firstly, an evaluation index system for distribution transformer status is constructed, and fuzzy binary quantification is performed on the state variables.The relationship between the state variables and the fault is explored by the fuzzy Apriori algorithm, and the key state variables that induces transformer fault is extracted. The Tanimoto coefficient is introduced for the limited fault data of distribution transformers, and the effective auxiliary fault data is migrated to the target distribution transformer, on this basis, the fault diagnosis model of distribution transformer based on information migration is established. The health index is introduced to describe the distribution status and the auxiliary fault data with different health levels is migrated because data has expired, on this basis, the fault diagnosis model of distribution transformers with expired data was established. the weights of the target and auxiliary fault data in the above model are iteratively solved by using TrAdaBoost, and then the fault diagnostic model is output. Finally, an example analysis is carried on the basis of the distribution transformer fault data, the simulation results show that the fault diagnosis accuracy of the model in this paper is high, and it has stronger generalization ability than traditional diagnosis model.
Aiming at the problem of limited fault data and data expiration of distribution transformers, a transfer learning fault diagnosis model of distribution transformer considering multifactor situation evolution is proposed in this paper. Firstly, an evaluation index system for distribution transformer status is constructed, and fuzzy binary quantification is performed on the state variables.The relationship between the state variables and the fault is explored by the fuzzy Apriori algorithm, and the key state variables that induces transformer fault is extracted. The Tanimoto coefficient is introduced for the limited fault data of distribution transformers, and the effective auxiliary fault data is migrated to the target distribution transformer, on this basis, the fault diagnosis model of distribution transformer based on information migration is established. The health index is introduced to describe the distribution status and the auxiliary fault data with different health levels is migrated because data has expired, on this basis, the fault diagnosis model of distribution transformers with expired data was established. the weights of the target and auxiliary fault data in the above model are iteratively solved by using TrAdaBoost, and then the fault diagnostic model is output. Finally, an example analysis is carried on the basis of the distribution transformer fault data, the simulation results show that the fault diagnosis accuracy of the model in this paper is high, and it has stronger generalization ability than traditional diagnosis model.
引文
[1]Fan Jingmin,Wang Feng,Sun Qiuqin,et al.Hybrid RVM-ANFIS algorithm for transformer fault diagnosis[J].IET Generation Transmission&Distribution,2017,11(14):3637-3643.
[2]Malik H,Mishra S.Application of gene expression programming(GEP)in power transformers fault diagnosis using DGA[J].IEEE Transactions on Industry Applications,2016,52(6):4556-4565.
[3]吴广宁,袁海满,高波,等.基于特征评估与核主元分析的电力变压器故障诊断[J].高电压技术,2017,43(8):2533-2540.Wu Guangning,Yuan Haiman,Gao Bo,et al.Fault diagnosis of power transformer based on feature evaluation and kernel principal component analysis[J].High Voltage Engineering,2017,43(8):2533-2540.
[4]汪可,李金忠,张书琦,等.变压器故障诊断用油中溶解气体新特征参量[J].中国电机工程学报,2016,36(23):6570-6578.Wang Ke,Li Jinzhong,Zhang Shuqi,et al.New features derived from dissolved gas analysis for fault diagnosis of power transformers[J].Proceedings of the CSEE,2016,36(23):6570-6578.
[5]谢龙君,李黎,程勇,等.融合集对分析和关联规则的变压器故障诊断方法[J].中国电机工程学报,2015,35(2):277-286.Xie Longjun,Li Li,Cheng Yong,et al.A fault diagnosis method of power transformers by integrated set pair analysis and association rules[J].Proceedings of the CSEE,2015,35(2):277-286.
[6]郑元兵,孙才新,李剑,等.变压器故障特征量可信度的关联规则分析[J].高电压技术,2012,38(1):82-88.Zheng Yuanbing,Sun Caixin,Li Jian,et al.Association rule analysis on confidence of features for transformer faults[J].High Voltage Engineering,2012,38(1):82-88.
[7]李赢,舒乃秋.基于模糊聚类和完全二又树支持向量机的变压器故障诊断[J].电工技术学报,2016,31(4):64-70.Li Ying,Shu Naiqiu.Transformer fault diagnosis based on fuzzy clustering and complete binary tree support vector machine[J].Transactions of China Electrotechnical Society,2016,31(4):64-70.
[8]Chen Weigen,Pan Chong,Yun Yuxin,et al.Wavelet networks in power transformers diagnosis using dissolved gas analysis[J].IEEE Transactions on Power Delivery,2008,24(1):187-194.
[9]苑津莎,张利伟,李中,等.基于互补免疫算法的变压器故障诊断[J].电工技术学报,2015,30(24):67-75.Yuan Jinsha,Zhang Liwei,Li Zhong,et al.Fault diagnosis of transformers based on complementary immune algorithm[J].Transactions of China Electrotechnical Society,2015,30(24):67-75.
[10]施恂山,马宏忠,张琳,等.PSO改进RBPNN在变压器故障诊断中的应用[J].电力系统保护与控制,2016,44(17):39-44.Shi Xunshan,Ma Hongzhong,Zhang Lin,et al.Application of RBPNN improved by PSO in fault diagnosis of transformers[J].Power System Protection and Control,2016,44(17):39-44.
[11]Liu Cheng,Niu Rui,Fan Heming,et al.Transformer fault diagnosis in parallel based on the spark platform[J].Electric Power Science&Engineering,2016(6):36-41.
[12]梁永亮,李可军,牛林,等.一种优化特征选择-快速相关向量机变压器故障诊断方法[J].电网技术,2013,37(11):3262-3267.Liang Yongliang,Li Kejun,Niu Lin,et al.A transformer diagnosis method based on optimized feature selection methods and fast relevance vector machine[J].Power System Technology,2013,37(11):3262-3267.
[13]董保国.基于图像处理的变压器渗漏油检测[J].电力建设,2013,34(11):121-124.Dong Baoguo.Detection of transformer oil leakage based on image processing[J].Electric Power Construction,2013,34(11):121-124.
[14]朱永利,贾亚飞,王刘旺,等.基于改进变分模态分界和Hilbert变换的变压器局部放电信号特征提取及分类[J].电工技术学报,2017,32(9):221-234.Zhu Yongli,Jia Yafen,Wang Liuwang,et al.Feature extraction and classification on partial discharge signals of power transformers based on improved variational mode decomposition and hilbert transform[J].Transactions of China Electrotechnical Society,2017,32(9):221-234.
[15]孙莹,高贺,李可军,等.基于多时段信息融合的配电变压器运行状态评估模型[J].高电压技术,2016,42(7):2054-2062.Sun Ying,Gao He,Li Kejun,et al.Condition assessment model of distribution transformer based on multi-period information fusion[J].High Voltage Engineering,2016,42(7):2054-2062.
[16]刘兴平,陈民铀.一种配电变压器绕组变形故障的在线监测新方法[J].电力系统保护与控制,2013,41(12):20-26.Liu Xingping,Chen Minyou.A novel method on online monitoring of winding deformation of transformers[J].Power System Protection and Control,2013,41(12):20-26.
[17]张友强,寇凌峰,盛万兴,等.配电变压器运行状态评估的大数据分析方法[J].电网技术,2016,40(3):768-773.Zhang Youqiang,Kou Lingfeng,Sheng Wanxing,et al.Big data analytical method for operating state assessment of distribution transformer[J].Power System Technology,2016,40(3):768-773.
[18]Qin Hao,Zhou Wenyou,Zhang Minzhi,et al.Research on fault diagnosis method of distribution transformer based on MFCC and HMM[C]//Proceedings of the 2016 4th International Conference on Sensors,Mechatronics and Automation,Zhuhai,China,2016:1-5.
[19]董爱美,王士同.共享隐空间迁移SVM[J].自动化学报,2014,40(10):2276-2287.Dong Aimei,Wang Shitong.A shared latent subspace transfer learning algorithm using SVM[J].Acta Automatica Sinica,2014,40(10):2276-2287.
[20]王振亚.模糊关联规则在推荐系统的应用研究[D].北京:中国地质大学(北京),2016.
[21]程学珍,林晓晓,朱春华,等.基于时序信息的模糊Petri网电网故障诊断方法[J].电工技术学报,2017,32(14):229-237.Cheng Xuezhen,Lin Xiaoxiao,Zhu Chunhua,et al.Power system fault analysis based on hierarchical fuzzy Petri net considering time association character[J].Transactions of China Electrotechnical Society,2017,32(14):229-237.
[22]Pan S J,Yang Qiang.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[23]邱志斌,阮江军,黄道春,等.基于支持向量机的棒-板空气间隙击穿电压预测方法及其应用[J].电工技术学报,2017,32(19):220-228.Qiu Zhibin,Ruan Jiangjun,Huang Daochun,et al.Breakdown voltage prediction method of rod-plane air gaps based on support vector machine and its applications[J].Transactions of China Electrotechnical Society,2017,32(19):220-228.
[24]李奎,李晓倍,郑淑梅,等.基于BP神经网络的交流接触器剩余电寿命预测[J].电工技术学报,2017,32(15):120-127.Li Kui,Li Xiaobei,Zheng Shumei,et al.Residual electrical life prediction for AC contactor based on BPneural network[J].Transactions of China Electrotechnical Society,2017,32(15):120-127.
[25]李恩文,王力农,宋斌,等.基于改进模糊聚类算法的变压器油色谱分析[J].电工技术学报,2018,33(19):4594-4602.Li Enwen,Wang Linong,Song Bin,et al.Analysis of transformer oil chromatography based on improved fuzzy clustering algorithm[J].Transactions of China Electrotechnical Society,2018,33(19):4594-4602.
[26]李喜桂,常燕,罗运柏,等.基于健康指数的变压器剩余寿命评估[J].高压电器,2012,48(12):80-85.Li Xigui,Chang Yan,Luo Yunbai,et al.Remnant life estimation of power transformer based on health index[J].High Voltage Apparatus,2012,48(12):80-85.
[2]Malik H,Mishra S.Application of gene expression programming(GEP)in power transformers fault diagnosis using DGA[J].IEEE Transactions on Industry Applications,2016,52(6):4556-4565.
[3]吴广宁,袁海满,高波,等.基于特征评估与核主元分析的电力变压器故障诊断[J].高电压技术,2017,43(8):2533-2540.Wu Guangning,Yuan Haiman,Gao Bo,et al.Fault diagnosis of power transformer based on feature evaluation and kernel principal component analysis[J].High Voltage Engineering,2017,43(8):2533-2540.
[4]汪可,李金忠,张书琦,等.变压器故障诊断用油中溶解气体新特征参量[J].中国电机工程学报,2016,36(23):6570-6578.Wang Ke,Li Jinzhong,Zhang Shuqi,et al.New features derived from dissolved gas analysis for fault diagnosis of power transformers[J].Proceedings of the CSEE,2016,36(23):6570-6578.
[5]谢龙君,李黎,程勇,等.融合集对分析和关联规则的变压器故障诊断方法[J].中国电机工程学报,2015,35(2):277-286.Xie Longjun,Li Li,Cheng Yong,et al.A fault diagnosis method of power transformers by integrated set pair analysis and association rules[J].Proceedings of the CSEE,2015,35(2):277-286.
[6]郑元兵,孙才新,李剑,等.变压器故障特征量可信度的关联规则分析[J].高电压技术,2012,38(1):82-88.Zheng Yuanbing,Sun Caixin,Li Jian,et al.Association rule analysis on confidence of features for transformer faults[J].High Voltage Engineering,2012,38(1):82-88.
[7]李赢,舒乃秋.基于模糊聚类和完全二又树支持向量机的变压器故障诊断[J].电工技术学报,2016,31(4):64-70.Li Ying,Shu Naiqiu.Transformer fault diagnosis based on fuzzy clustering and complete binary tree support vector machine[J].Transactions of China Electrotechnical Society,2016,31(4):64-70.
[8]Chen Weigen,Pan Chong,Yun Yuxin,et al.Wavelet networks in power transformers diagnosis using dissolved gas analysis[J].IEEE Transactions on Power Delivery,2008,24(1):187-194.
[9]苑津莎,张利伟,李中,等.基于互补免疫算法的变压器故障诊断[J].电工技术学报,2015,30(24):67-75.Yuan Jinsha,Zhang Liwei,Li Zhong,et al.Fault diagnosis of transformers based on complementary immune algorithm[J].Transactions of China Electrotechnical Society,2015,30(24):67-75.
[10]施恂山,马宏忠,张琳,等.PSO改进RBPNN在变压器故障诊断中的应用[J].电力系统保护与控制,2016,44(17):39-44.Shi Xunshan,Ma Hongzhong,Zhang Lin,et al.Application of RBPNN improved by PSO in fault diagnosis of transformers[J].Power System Protection and Control,2016,44(17):39-44.
[11]Liu Cheng,Niu Rui,Fan Heming,et al.Transformer fault diagnosis in parallel based on the spark platform[J].Electric Power Science&Engineering,2016(6):36-41.
[12]梁永亮,李可军,牛林,等.一种优化特征选择-快速相关向量机变压器故障诊断方法[J].电网技术,2013,37(11):3262-3267.Liang Yongliang,Li Kejun,Niu Lin,et al.A transformer diagnosis method based on optimized feature selection methods and fast relevance vector machine[J].Power System Technology,2013,37(11):3262-3267.
[13]董保国.基于图像处理的变压器渗漏油检测[J].电力建设,2013,34(11):121-124.Dong Baoguo.Detection of transformer oil leakage based on image processing[J].Electric Power Construction,2013,34(11):121-124.
[14]朱永利,贾亚飞,王刘旺,等.基于改进变分模态分界和Hilbert变换的变压器局部放电信号特征提取及分类[J].电工技术学报,2017,32(9):221-234.Zhu Yongli,Jia Yafen,Wang Liuwang,et al.Feature extraction and classification on partial discharge signals of power transformers based on improved variational mode decomposition and hilbert transform[J].Transactions of China Electrotechnical Society,2017,32(9):221-234.
[15]孙莹,高贺,李可军,等.基于多时段信息融合的配电变压器运行状态评估模型[J].高电压技术,2016,42(7):2054-2062.Sun Ying,Gao He,Li Kejun,et al.Condition assessment model of distribution transformer based on multi-period information fusion[J].High Voltage Engineering,2016,42(7):2054-2062.
[16]刘兴平,陈民铀.一种配电变压器绕组变形故障的在线监测新方法[J].电力系统保护与控制,2013,41(12):20-26.Liu Xingping,Chen Minyou.A novel method on online monitoring of winding deformation of transformers[J].Power System Protection and Control,2013,41(12):20-26.
[17]张友强,寇凌峰,盛万兴,等.配电变压器运行状态评估的大数据分析方法[J].电网技术,2016,40(3):768-773.Zhang Youqiang,Kou Lingfeng,Sheng Wanxing,et al.Big data analytical method for operating state assessment of distribution transformer[J].Power System Technology,2016,40(3):768-773.
[18]Qin Hao,Zhou Wenyou,Zhang Minzhi,et al.Research on fault diagnosis method of distribution transformer based on MFCC and HMM[C]//Proceedings of the 2016 4th International Conference on Sensors,Mechatronics and Automation,Zhuhai,China,2016:1-5.
[19]董爱美,王士同.共享隐空间迁移SVM[J].自动化学报,2014,40(10):2276-2287.Dong Aimei,Wang Shitong.A shared latent subspace transfer learning algorithm using SVM[J].Acta Automatica Sinica,2014,40(10):2276-2287.
[20]王振亚.模糊关联规则在推荐系统的应用研究[D].北京:中国地质大学(北京),2016.
[21]程学珍,林晓晓,朱春华,等.基于时序信息的模糊Petri网电网故障诊断方法[J].电工技术学报,2017,32(14):229-237.Cheng Xuezhen,Lin Xiaoxiao,Zhu Chunhua,et al.Power system fault analysis based on hierarchical fuzzy Petri net considering time association character[J].Transactions of China Electrotechnical Society,2017,32(14):229-237.
[22]Pan S J,Yang Qiang.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[23]邱志斌,阮江军,黄道春,等.基于支持向量机的棒-板空气间隙击穿电压预测方法及其应用[J].电工技术学报,2017,32(19):220-228.Qiu Zhibin,Ruan Jiangjun,Huang Daochun,et al.Breakdown voltage prediction method of rod-plane air gaps based on support vector machine and its applications[J].Transactions of China Electrotechnical Society,2017,32(19):220-228.
[24]李奎,李晓倍,郑淑梅,等.基于BP神经网络的交流接触器剩余电寿命预测[J].电工技术学报,2017,32(15):120-127.Li Kui,Li Xiaobei,Zheng Shumei,et al.Residual electrical life prediction for AC contactor based on BPneural network[J].Transactions of China Electrotechnical Society,2017,32(15):120-127.
[25]李恩文,王力农,宋斌,等.基于改进模糊聚类算法的变压器油色谱分析[J].电工技术学报,2018,33(19):4594-4602.Li Enwen,Wang Linong,Song Bin,et al.Analysis of transformer oil chromatography based on improved fuzzy clustering algorithm[J].Transactions of China Electrotechnical Society,2018,33(19):4594-4602.
[26]李喜桂,常燕,罗运柏,等.基于健康指数的变压器剩余寿命评估[J].高压电器,2012,48(12):80-85.Li Xigui,Chang Yan,Luo Yunbai,et al.Remnant life estimation of power transformer based on health index[J].High Voltage Apparatus,2012,48(12):80-85.