基于DTCWT-DBN的配电网内部过电压类型识别
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摘要
提出一种基于双树复小波变换(DTCWT)-深度信念网络(DBN)的配电网内部过电压识别方法。将10 kV母线三相过电压信号进行双树复小波变换,再通过奇异值分解降维,将所得奇异值作为特征值输入训练好的深度信念网络分类器,实现对7种典型的内部过电压的类型识别。利用ATP/EMTP仿真数据和物理实验平台上的故障波形对所提算法进行训练和测试,并将之与其他分类算法进行对比。结果表明,相较于所列举的其他方法,所提算法具有更强的特征提取能力和更高的识别准确率。
A method based on Double Tree Complex Wavelet Transform(DTCWT)-Deep Belief Network(DBN) for overvoltage identification in distribution network is proposed. The three-phase overvoltage signal of the 10 kV bus is subjected to the dual-tree complex wavelet transform, and then the singular value is reduced by the singular value decomposition. The resulting singular value is input into the trained deep belief network classifier as the eigenvalue, and the seven typical internal overvoltage type identification types are realized. The proposed algorithm is trained and tested using ATP/EMTP simulation data and fault waveforms on the physics experiment platform, and compared with other classification algorithms. The results show that compared with other methods listed in this paper, the proposed algorithm has stronger feature extraction capability and higher recognition accuracy.
A method based on Double Tree Complex Wavelet Transform(DTCWT)-Deep Belief Network(DBN) for overvoltage identification in distribution network is proposed. The three-phase overvoltage signal of the 10 kV bus is subjected to the dual-tree complex wavelet transform, and then the singular value is reduced by the singular value decomposition. The resulting singular value is input into the trained deep belief network classifier as the eigenvalue, and the seven typical internal overvoltage type identification types are realized. The proposed algorithm is trained and tested using ATP/EMTP simulation data and fault waveforms on the physics experiment platform, and compared with other classification algorithms. The results show that compared with other methods listed in this paper, the proposed algorithm has stronger feature extraction capability and higher recognition accuracy.
引文
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[12]郭森.基于BN分解和ALO优化LSSVM模型的风电出力预测[J].智慧电力,2017,45(7):92-99.GUO Sen.Wind power forecasting based on BNdecomposition and LSSVM model optimized by ALO[J].Smart Power,2017,45(7):92-99.
[13]宋宗耘,牛东晓,肖鑫利,等.基于改进萤火虫算法优化SVM的变电工程造价预测[J].中国电力,2017,50(3):168-173.SONG Zongyun,NIU Dongxiao,XIAO Xinli,et al.Substation engineering cost forecasting method based on modified firefly algorithm and support vector machine[J].Electric Power,2017,50(3):168-173.
[14]薛阳,杜新纲,张蓬鹤,等.电能表故障与地域气候、行业负荷关系研究[J].中国电力,2017,50(8):98-105.XUE Yang,DU Xingang,ZHANG Penghe,et al.Research on the relationship between electric energy meter fault and regional climate&load in different industries[J].Electric Power,2017,50(8):98-105.
[15]齐郑,张惠汐,饶志,等.基于极限学习机的多信息融合区段定位方法[J].电力系统保护与控制,2014,42(19):74-80.QI Zheng,ZHANG Huixi,RAO Zhi,et al.Multiinformation fusion fault location based on extreme learning machine[J].Power System Protection and Control,2014,42(19):74-80.
[16]冯义,刘慧文,张宝平,等.基于集合经验模态分解和特征选择极端学习机的风速预测[J].智慧电力,2018,46(12):30-37.FENG Yi,LIU Huiwen,ZHANG Baoping,et al.Shortterm wind speed forecasting using ensemble empirical mode decomposition and extreme learning machine with feature selection[J].Smart Power,2018,46(12):30-37.
[17]张淑清,胡永涛,姜安琦,等.基于双树复小波和自适应权重和时间因子的粒子群优化支持向量机的轴承故障诊断[J].中国机械工程,2017,28(3):327-333.ZHANG Shuqing,HU Yongtao,JIANG Anqi,et al.Bearing fault diagnosis based on DTCWT and AWTFPSO-optimized SVM[J].China Mechanical Engineering,2017,28(3):327-333.
[18]卢其威,王涛,李宗睿,等.基于小波变换和奇异值分解的串联电弧故障检测方法[J].电工技术学报,2017,32(17):208-217.LU Qiwei,WANG Tao,LI Zongrui,et al.Detection method of series arcing fault based on wavelet transform and singular value decomposition[J].Transactions of China Electrotechnical Society,2017,32(17):208-217.
[19]石鑫,朱永利,萨初日拉,等.基于深度信念网络的电力变压器故障分类建模[J].电力系统保护与控制,2016,44(1):71-76.SHI Xin,ZHU Yongli,SA Churila,et al.Power transformer fault classifying model based on deep belief network[J].Power System Protection and Control,2016,44(1):71-76.
[20]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2014,18(7):1527-1554.
[21]单外平,曾雪琼.基于深度信念网络的信号重构与轴承故障识别[J].电子设计工程,2016,24(4):67-71.SHAN Waiping,ZENG Xueqiong.Signal reconstruction and bearing fault identification based on deep belief network[J].Electronic Design Engineering,2016,24(4):67-71.
[22]毛勇华,桂小林,李前,等.深度学习应用技术研究[J].计算机应用研究,2016,33(11):3201-3205.MAO Yonghua,GUI Xiaolin,LI Qian,et al.Study on application technology of deep learning[J].Application Research of Computer,2016,33(11):3201-3205.
[23]高伟,陈伟凡,杨耿杰,等.基于奇异值分解和多级支持向量机的配电网故障类型识别[J].电子测量与仪器学报,2018,32(2):62-71.GAO Wei,CHEN Weifan,YANG Gengjie,et al.Fault type identification for distribution network based on singular value decomposition and multi-level support vector machine[J].Journal of Electronic Measurement and Instrumentation,2018,32(2):62-71.
[24]GUO M F,YANG N C.Features-clustering-based earth fault detection using singular-value decomposition and fuzzy c-means in resonant grounding distribution systems[J].International Journal of Electrical Power&Energy Systems,2017,93:97-108.
[2]郑志宇,蔡翀,张昭丞,等.基于小波域相子的电压暂降特征提取与成因辨识[J].电力系统保护与控制,2018,46(1):16-22.ZHENG Zhiyu,CAI Chong,ZHANG Zhaocheng,et al.Wavelet-based phasor to detect and identify the voltage sag characteristics[J].Power System Protection and Control,2018,46(1):16-22.
[3]杜林,李欣,吴高林,等.采用3类特征参量比值法的铁磁谐振过电压识别[J].高电压技术,2011,37(9):2241-2249.DU Lin,LI Xin,WU Gaolin,et al.Ferro-resonance overvoltage identification using three feature parameter of ratio method[J].High Voltage Engineering,2011,37(9):2241-2249.
[4]覃星福,龚仁喜.基于广义S变换与PSO-PNN的电能质量扰动识别[J].电力系统保护与控制,2016,44(15):10-17.QIN Xingfu,GONG Renxi.Power quality disturbance classification based on generalized S-transform and PSO-PNN[J].Power System Protection and Control,2016,44(15):10-17.
[5]杜林,戴斌,陆国俊,等.基于S变换局部奇异值分解的过电压特征提取[J].电工技术学报,2010,25(12):147-153.DU Lin,DAI Bin,LU Guojun,et al.Overvoltage features extraction based on S-Transform and local singular value decomposition[J].Transactions of China Electrotechnical Society,2010,25(12):147-153.
[6]朱永利,贾亚飞,王刘旺,等.基于改进变分模态分解和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):225-239.ZHU Yongli,JIA Yafei,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):225-239.
[7]郭谋发,游林旭,洪翠,等.基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法[J].高电压技术,2017,43(4):1239-1247.GUO Moufa,YOU Linxu,HONG Cui,et al.Identification method of distribution network faults based on singular value of LCD-Hilbert spectrums and multilevel SVM[J].High Voltage Engineering,2017,43(4):1239-1247.
[8]司马文霞,王荆,杨庆,等.Hilbert-Huang变换在电力系统过电压识别中的应用[J].高电压技术,2010,36(6):1480-1486.SIMA Wenxia,WANG Jing,YANG Qing,et al.Application of Hilbert-Huang transform to power system over-voltage recognition[J].High Voltage Engineering,2010,36(6):1480-1486.
[9]陈炜,方志广.输电线路的雷电过电压的识别方法[J].自动化与仪器仪表,2016(6):69-72.CHEN Wei,FANG Zhiguang.Lightning overvoltage identification method for transmission lines[J].Automation and Instrumentation,2016(6):69-72.
[10]任子晖,王琦.基于优化DDAGSVM多类分类策略的电能质量扰动识别[J].电力系统保护与控制,2018,46(5):82-88.REN Zihui,WANG Qi.Power quality disturbance recognition based on improved DDAGSVM multi-class classification strategy[J].Power System Protection and Control,2018,46(5):82-88.
[11]HOU K,SHAO G,WANG H,et al.Research on practical power system stability analysis algorithm based on modified SVM[J].Protection and Control of Modern Power Systems,2018,3(3):119-125.DOI:10.1186/s41601-018-0086-0.
[12]郭森.基于BN分解和ALO优化LSSVM模型的风电出力预测[J].智慧电力,2017,45(7):92-99.GUO Sen.Wind power forecasting based on BNdecomposition and LSSVM model optimized by ALO[J].Smart Power,2017,45(7):92-99.
[13]宋宗耘,牛东晓,肖鑫利,等.基于改进萤火虫算法优化SVM的变电工程造价预测[J].中国电力,2017,50(3):168-173.SONG Zongyun,NIU Dongxiao,XIAO Xinli,et al.Substation engineering cost forecasting method based on modified firefly algorithm and support vector machine[J].Electric Power,2017,50(3):168-173.
[14]薛阳,杜新纲,张蓬鹤,等.电能表故障与地域气候、行业负荷关系研究[J].中国电力,2017,50(8):98-105.XUE Yang,DU Xingang,ZHANG Penghe,et al.Research on the relationship between electric energy meter fault and regional climate&load in different industries[J].Electric Power,2017,50(8):98-105.
[15]齐郑,张惠汐,饶志,等.基于极限学习机的多信息融合区段定位方法[J].电力系统保护与控制,2014,42(19):74-80.QI Zheng,ZHANG Huixi,RAO Zhi,et al.Multiinformation fusion fault location based on extreme learning machine[J].Power System Protection and Control,2014,42(19):74-80.
[16]冯义,刘慧文,张宝平,等.基于集合经验模态分解和特征选择极端学习机的风速预测[J].智慧电力,2018,46(12):30-37.FENG Yi,LIU Huiwen,ZHANG Baoping,et al.Shortterm wind speed forecasting using ensemble empirical mode decomposition and extreme learning machine with feature selection[J].Smart Power,2018,46(12):30-37.
[17]张淑清,胡永涛,姜安琦,等.基于双树复小波和自适应权重和时间因子的粒子群优化支持向量机的轴承故障诊断[J].中国机械工程,2017,28(3):327-333.ZHANG Shuqing,HU Yongtao,JIANG Anqi,et al.Bearing fault diagnosis based on DTCWT and AWTFPSO-optimized SVM[J].China Mechanical Engineering,2017,28(3):327-333.
[18]卢其威,王涛,李宗睿,等.基于小波变换和奇异值分解的串联电弧故障检测方法[J].电工技术学报,2017,32(17):208-217.LU Qiwei,WANG Tao,LI Zongrui,et al.Detection method of series arcing fault based on wavelet transform and singular value decomposition[J].Transactions of China Electrotechnical Society,2017,32(17):208-217.
[19]石鑫,朱永利,萨初日拉,等.基于深度信念网络的电力变压器故障分类建模[J].电力系统保护与控制,2016,44(1):71-76.SHI Xin,ZHU Yongli,SA Churila,et al.Power transformer fault classifying model based on deep belief network[J].Power System Protection and Control,2016,44(1):71-76.
[20]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2014,18(7):1527-1554.
[21]单外平,曾雪琼.基于深度信念网络的信号重构与轴承故障识别[J].电子设计工程,2016,24(4):67-71.SHAN Waiping,ZENG Xueqiong.Signal reconstruction and bearing fault identification based on deep belief network[J].Electronic Design Engineering,2016,24(4):67-71.
[22]毛勇华,桂小林,李前,等.深度学习应用技术研究[J].计算机应用研究,2016,33(11):3201-3205.MAO Yonghua,GUI Xiaolin,LI Qian,et al.Study on application technology of deep learning[J].Application Research of Computer,2016,33(11):3201-3205.
[23]高伟,陈伟凡,杨耿杰,等.基于奇异值分解和多级支持向量机的配电网故障类型识别[J].电子测量与仪器学报,2018,32(2):62-71.GAO Wei,CHEN Weifan,YANG Gengjie,et al.Fault type identification for distribution network based on singular value decomposition and multi-level support vector machine[J].Journal of Electronic Measurement and Instrumentation,2018,32(2):62-71.
[24]GUO M F,YANG N C.Features-clustering-based earth fault detection using singular-value decomposition and fuzzy c-means in resonant grounding distribution systems[J].International Journal of Electrical Power&Energy Systems,2017,93:97-108.