电力变压器局部放电信号的特征提取与模式识别方法研究
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摘要
电力变压器是电力系统中的关键设备,维护其安全稳定的运行至关重要。局部放电是变压器绝缘劣化的重要征兆及表现形式,有效检测局部放电对于正确评估变压器的绝缘状况具有重要的现实意义。本文基于变压器局部放电的原理及特性,深入研究了局部放电信号的干扰抑制、特征提取以及模式识别方法。主要研究工作如下：
提出了基于平移不变小波迹的变压器局部放电信号消噪方法。深入分析了小波迹方法的消噪原理,将其应用于局部放电信号去噪,并利用循环平移方法消除小波基的平移依赖性,抑制小波变换产生的振荡现象,强化消噪效果。仿真及现场信号分析验证了该方法的有效性。
针对传统局部放电特征提取方法对噪声信号的高敏感性,提出了基于交叉小波变换的局部放电特征提取方法。研究了交叉小波变换对信号在时频域内的分析特性,采用该方法对局部放电信号进行处理,得到描述交叉谱图特性的放电特征参数,并利用相关系数矩阵对特征参数进行相关性分析。实例分析表明,该特征提取方法可以有效避免噪声信号的影响。
提出了基于主动学习SVM的局部放电模式识别方法。将主动学习的思想引用到“一对一”多分类SVM分类器,选用基于后验概率的采样函数对放电样本进行选择,挑选出对分类器最有价值的样本进行训练。实例分析表明,在保证局部放电识别精度的前提下,该方法可以减少训练样本个数,提高样本学习效率。
深入分析了主成分分析(PCA)方法的原理,利用PCA对局部放电的高维统计特征参数进行处理,提取出较少的主成分因子来表征原始信号特征,并通过相关向量机(RVM)对降维前后的特征参数进行模式识别。实例分析验证了该降维方法的有效性。
提出了基于多核多分类相关向量机(MMRVM)的局部放电模式识别方法。该方法采用不同的核函数融合多个不同的放电数据源信息,并利用粒子群优化算法对组合核参数进行寻优配置,选取出最优的核参数。局部放电实验数据分析表明,设计出的MMRVM分类模型融合了多种放电特征信息,能够较为全面的描述局部放电特性,具有较高的诊断准确率。
Power transformer is the key equipment in the electrical system and it is important to maintain its safety and stable operation. Partial discharge is the important symptom and manifestation of insulation deterioration in power transformer. Effective detection of partial discharge has practical significance in insulation evaluation of power transformer. Deep studies on interference suppression method, feature extraction method and pattern recognition method for partial discharge were conducted based on the principles and properties of partial discharge signal. The main work of this dissertation is as follows.
A denoising method for partial discharge signal in power transformer based on translation invariant wavelet footprint was proposed. The denoising principle of wavelet footprint was studied and the method was applied in denoising of partial discharge signal. Cycle spanning method was used for eliminating the translation dependence, so as to restrain the oscillation phenomenon and strengthen denoising effection. Effectiveness of the method was validated by case studies.
A novel feature extraction method for partial discharge based on cross-wavelet transform was proposed, aimed at the high sensitivity of the traditional feature extraction method to noise. The analytical characteristics of signal in time and frequency domain were described by cross-wavelet transform. The method was used for processing of the partial discharge signal and the feature parameters were obtained which represented the characteristics of the cross diagram. Finally the correlation coefficient matrix method was applied to correlation analysis between feature parameters. Case analysis demonstrated that the proposed method could effectively avoid the influence of noise.
A pattern recognition method for partial discharge based on active learning SVM was proposed. The idea of active learning was applied to "one against one" multi-class SVM classifier. The sampling function based on posterior probability was used for sample selection. Those samples which were valuable to the classifier were selected for training. Case analysis showed that, the proposed method could reduce the number of training samples and improve the learning efficiency on the premise of maintaining high recognition accuracy.
The theory of principal component analysis (PCA) method was considered for deep analysis. PCA method was used for processing high-dimensional statistical parameters. Fewer principal component factors were extracted to represent original signal characteristics. The relevance vetor machine (RVM) classifier was applied to pattern recognition with the characteristic parameters before and after dimension reduction. The effectiveness of the proposed method for dimension reduction was validated by case analysis.
A pattern recognition method for partial discharge based on the multi-kernel multi-class relevance vetor machine (MMRVM) was proposed. The method integrated information of different discharge sources with different kernel functions. Particle swarm optimization algorithm was utilized for optimal design to obtain the optimal combination parameters. Experimental data of partial discharge indicated that the designed classification model integrated various feature information and could represent partial discharge characteristics comprehensively with higher diagnostic accuracy.
提出了基于平移不变小波迹的变压器局部放电信号消噪方法。深入分析了小波迹方法的消噪原理,将其应用于局部放电信号去噪,并利用循环平移方法消除小波基的平移依赖性,抑制小波变换产生的振荡现象,强化消噪效果。仿真及现场信号分析验证了该方法的有效性。
针对传统局部放电特征提取方法对噪声信号的高敏感性,提出了基于交叉小波变换的局部放电特征提取方法。研究了交叉小波变换对信号在时频域内的分析特性,采用该方法对局部放电信号进行处理,得到描述交叉谱图特性的放电特征参数,并利用相关系数矩阵对特征参数进行相关性分析。实例分析表明,该特征提取方法可以有效避免噪声信号的影响。
提出了基于主动学习SVM的局部放电模式识别方法。将主动学习的思想引用到“一对一”多分类SVM分类器,选用基于后验概率的采样函数对放电样本进行选择,挑选出对分类器最有价值的样本进行训练。实例分析表明,在保证局部放电识别精度的前提下,该方法可以减少训练样本个数,提高样本学习效率。
深入分析了主成分分析(PCA)方法的原理,利用PCA对局部放电的高维统计特征参数进行处理,提取出较少的主成分因子来表征原始信号特征,并通过相关向量机(RVM)对降维前后的特征参数进行模式识别。实例分析验证了该降维方法的有效性。
提出了基于多核多分类相关向量机(MMRVM)的局部放电模式识别方法。该方法采用不同的核函数融合多个不同的放电数据源信息,并利用粒子群优化算法对组合核参数进行寻优配置,选取出最优的核参数。局部放电实验数据分析表明,设计出的MMRVM分类模型融合了多种放电特征信息,能够较为全面的描述局部放电特性,具有较高的诊断准确率。
Power transformer is the key equipment in the electrical system and it is important to maintain its safety and stable operation. Partial discharge is the important symptom and manifestation of insulation deterioration in power transformer. Effective detection of partial discharge has practical significance in insulation evaluation of power transformer. Deep studies on interference suppression method, feature extraction method and pattern recognition method for partial discharge were conducted based on the principles and properties of partial discharge signal. The main work of this dissertation is as follows.
A denoising method for partial discharge signal in power transformer based on translation invariant wavelet footprint was proposed. The denoising principle of wavelet footprint was studied and the method was applied in denoising of partial discharge signal. Cycle spanning method was used for eliminating the translation dependence, so as to restrain the oscillation phenomenon and strengthen denoising effection. Effectiveness of the method was validated by case studies.
A novel feature extraction method for partial discharge based on cross-wavelet transform was proposed, aimed at the high sensitivity of the traditional feature extraction method to noise. The analytical characteristics of signal in time and frequency domain were described by cross-wavelet transform. The method was used for processing of the partial discharge signal and the feature parameters were obtained which represented the characteristics of the cross diagram. Finally the correlation coefficient matrix method was applied to correlation analysis between feature parameters. Case analysis demonstrated that the proposed method could effectively avoid the influence of noise.
A pattern recognition method for partial discharge based on active learning SVM was proposed. The idea of active learning was applied to "one against one" multi-class SVM classifier. The sampling function based on posterior probability was used for sample selection. Those samples which were valuable to the classifier were selected for training. Case analysis showed that, the proposed method could reduce the number of training samples and improve the learning efficiency on the premise of maintaining high recognition accuracy.
The theory of principal component analysis (PCA) method was considered for deep analysis. PCA method was used for processing high-dimensional statistical parameters. Fewer principal component factors were extracted to represent original signal characteristics. The relevance vetor machine (RVM) classifier was applied to pattern recognition with the characteristic parameters before and after dimension reduction. The effectiveness of the proposed method for dimension reduction was validated by case analysis.
A pattern recognition method for partial discharge based on the multi-kernel multi-class relevance vetor machine (MMRVM) was proposed. The method integrated information of different discharge sources with different kernel functions. Particle swarm optimization algorithm was utilized for optimal design to obtain the optimal combination parameters. Experimental data of partial discharge indicated that the designed classification model integrated various feature information and could represent partial discharge characteristics comprehensively with higher diagnostic accuracy.
引文
[1]王昌长,李福祺,高胜友.电力设备的在线监测与故障诊断[M].北京：清华大学出版社,2006
[2]王致杰,徐余法,刘三明,等.电力设备状态监测与故障诊断[M].上海：上海交通大学出版社,2012
[3]操郭奎,许维宗,阮国方.变压器运行维护与故障分析处理[M].北京：中国电力出版社,2008
[4]王梦云.110 kV及以上变压器事故与缺陷统计分析[J].供用电,2007,24(1)：1-5
[5]吴广宁.电气设备状态监测的理论与实践[M].北京：清华大学出版社,2005
[6]王晓莺.变压器故障与监测[M].北京：机械工业出版社,2004
[7]金卓睿.变压器局部放电超高频监测分形天线与最优小波去噪及信号识别研究[D].重庆：重庆大学,2008
[8]朱德恒,谈克雄.电绝缘诊断技术[M].北京：中国电力出版社,1998
[9]葛景滂,邱昌容.局部放电测量[M].北京：机械工业出版社,1984
[10]邱昌容,王乃庆.电工设备局部放电及测试技术[M].北京：机械工业出版社,1994
[11]Hettiwatte S. N., Wang Z. D., Crossley P. A. Investigation of propagation of partial discharges in power transformers and techniques for locating the discharge[J]. IEE Proceedings in Science, Measurement and Technology,2005, 152(1):25-30
[12]Okabe S., Ueta G, Wada H., et al. Partial discharge signal propagation characteristics inside the winding of oil-immersed power transformer using the three-winding transformer model in air[J]. IEEE Transactions on Dielectrics and Insulation,2011,18(6):2024-2031
[13]郭俊,吴广宁,张血琴,等.局部放电检测技术的现状和发展[J].电工技术学报,2005,20(2)：29-35
[14]Krivda A. Automated recognition of partial discharge[J]. IEEE Transactions on Dielectrics and Insulation,1995,2(5):796-821
[15]Dreuger F. H., Gulski E., Krivda A. Classification of partial discharge[J]. IEEE Transactions on Dielectrics and Insulation,1993,28(6):917-931
[16]Ma H., Chan J. C., Saha T. K., et al. Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources[J]. IEEE Transactions on Dielectrics and Insulation,2013,20(2):468-478
[17]GB/T 7354-2003,局部放电测量[S].中华人民共和国国家标准,2003
[1 8]桂峻峰,高文胜,谈克雄,等.脉冲电流法测量变压器局部放电的频带选择[J].高电压技术,2005,31(1)：45-46
[19]Breen H. J., Gulski E., Smit J. J., et al. Standardization of on-line VHF PD measurements on turbo generators[J]. IEEE Transactions on Dielectrics Electrical Insulation,2002,9(1):140-149
[20]Judd M. D., Yang L., Hunter I. B. B. Partial discharge monitoring of power transformers using UHF sensors Part I:sensors and signal interpretation[J]. IEEE Electrical Insulation Magazine,2005,21(2):5-14
[21]王国利,郑毅,郝艳捧.用于变压器局部放电检测的超高频传感器的初步研究[J].中国电机工程学报,2002,22(4)：154-160
[22]Judd M. D., Cleary G. P., Bennoch C. J. Applying UHF partial discharge detection to power transformers[J]. IEEE Power Engineering Review,2002, 22(8):57-59
[23]王晓宁,朱德恒.汽轮发电机局部放电在线监测技术的研究[J].大电机技术,2003(3)：1-4
[24]李燕青.超声波法检测电力变压器局部放电的研究[D].保定：华北电力大学,2004
[25]Kweon D. J., Chin S. B., Kwak H. R., et al. The analysis of ultrasonic signals by partial discharge and noise from the transformer[J]. IEEE transactions on Power Delivery,2005,20(3):1976-1983
[26]司文荣,李军浩,袁鹏,等.局部放电光测法的研究现状与发展[J].高压电器,2008,44(3)：261-264
[27]张晓星,任江波,故耀垓,等.SF6局部放电分解组分长光程红外检测[J].电工技术学报,2012,27(5)：70-76
[28]Gulski E. Digital analysis of partial discharges [J]. IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5):822-837
[29]梁流铭.变压器典型缺陷局部放电特性及与油中溶解气体对应关系研究[D].重庆：重庆大学,2009
[30]陈伟根,孙才新,廖瑞金,等.变压器局部放电与油中气体浓度的对应关系[J].重庆大学学报(自然科学版),2000,23(4)：86-89
[31]Muhamad N. A., Phung B. T., Blackburn T. R. Dissolved gas analysis (DGA) of partial discharge fault in bio-degradable transformer insulation oil[C]. Power Engineering Conference, Perth, Western Australia,2007:1-6
[32]唐炬,孙才新,宋胜利,等.局部放电信号中的白噪声和窄带干扰[J].高电压技术,2002,28(12)：8-10
[33]王立欣,诸定秋,蔡维铮.局部放电在线监测中基于小波变换的阈值消噪算法研究[J].电网技术,2003,27(4)：46-48,78
[34]Satish L., Nazneen B. Wavelet-based denoising of partial discharge signals buried in excessive noise and interference[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2003,10(2):354-367
[35]Yang L., Judd M. D., Bennoch C. J. Denoising UHF signal for PD detection in transformers based on wavelet technique[C].2004 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Boulder, CO, USA,2004: 166-169
[36]杨霁,李剑,王有元,等.变压器局部放电监测中的小波去噪方法[J].重庆大学学报,2004,27(10)：67-70
[37]孙才新,李新,杨永明.从白噪声中提取局部放电信号的小波变换方法研究[J].电工技术学报,1999,14(3)：47-50
[38]李天云,程思勇,李光.基于交叉验证的局部放电白噪声抑制新方法[J].电工技术学报,2008,23(1)：19-23
[39]Ma X., Zhou C., Kemp I. J. Automated wavelet selection and thresholding for PD detection[J]. IEEE Electrical Insulation Magazine,2002,18(2):37-47
[40]刘云鹏,律方成,李成榕,等.基于数学形态滤波器抑制局部放电窄带周期性干扰的研究[J].中国电机工程学报,2004,24(3)：169-173
[41]Gaouda A. M., El-Hag A., Abdel-Galil T. K., et al. On-line detection and measurement of partial discharge signals in a noisy environment[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(4):1162-1173
[42]孙才新,罗兵,杜林,等.变压器局部放电在线监测中抗电磁干扰的定向耦合差动平衡法的研究[J].中国电机工程学报,1998,18(5)：340-344
[43]Kopf U., Feser K. Rejection of narrow-band noise and repetitive pulse in on-site PD measurements [J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1995,2(6):1180-1191
[44]刘蕾,吴克伟,冯义,等.脉冲极性鉴别局部放电测试仪的研制[J].华东电力,2004,32(2)：7-9
[45]郭亮,黄宏新,成永红,等.嵌入式局部放电检测系统的数字滤波器设计[J].高电压技术,2007,33(8)：44-47,51
[46]杨霁.基于小波多尺度变换的局部放电去噪与识别方法研究[D].重庆：重庆大学,2004
[47]李剑,杨洋,程长奎,等.变压器局部放电监测逐层最优小波去噪算法[J]. 高电压技术,33(8)：56-60
[48]Li Jian, Cheng Changkui, Jiang Tianyan, et al. Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(2):543-549
[49]何明格,殷鹰,林丽君,等.基于小波迹和匹配追踪算法的超声波检测信号消噪[J].应用基础与工程科学学报,2011,19(2)：297-304
[50]Dragotti P. L., Vetterli M. Deconvolution with wavelet footprints for ill-posed inverse problems[C].2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA,2002:1257-1260
[51]Dragotti P. L., Vetterli M. Wavelet transform footprints:catching singularities for compression and denoising[C].2000 International Conference on Image Processing, Vancouver, BC, Canada,2000, (2):363-366
[52]Wei H. T., Jeoti V. A wavelet footprints-based compression scheme for ECG signals[C]. TENCON,2004:283-286
[53]胡文堂,高胜友,余绍峰,等.统计参数在变压器局部放电模式识别中的应用[J].高电压技术,2009,35(2)：277-281
[54]Gulski E., Kreuger F. H. Computer-aided recognition of discharge sources[J]. IEEE Transactions on Electrical Insulation,1992,27(1):82-92
[55]满玉岩,高文胜,高凯,等.发电机局部放电的统计特征识别[J].电工技术学报,2006,21(4)：41-45
[56]Mazroua A. A., Bartnikas R., Salama M. M. A. Discrimination between PD pulse shapes using different neural network paradigms[J]. IEEE Transaction on Electrical Insulation.1994,1(6):1119-1131
[57]Wang Zhenyuan, Zhu Deheng, Tan Kexiong, et al. PD monitor system for power generators[J]. IEEE Transaction on Electrical Insulation,1998, 5(6):850-856
[58]王国利,郝艳捧,李彦明.油中典型局部放电模型放电波形特征参数的提取[J].电网技术,2001,25(10)：34-37,46
[59]Cacciari ML, Contin A., Montanari G. C. Use of a mixed-weibull distribution for the identification of PD phenomena[J]. IEEE Transaction on Dielectrics and Electrical Insulation,1995,2(4):614-627
[60]Contin A., Montanari G. C., Ferraro C. PD source recognition by Weibull processing of pulse height distribution[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2000,7(1):48-58
[61]Sarker N., Chaudhuri B. B. An efficient differential box-counting approach to compute fractal dimension of image [J]. IEEE Transactions on Systems, Man and Cybernetics,1994,24(1):115-120
[62]Pitsikalis V., Maragos P. Filtered dynamics and fractal dimensions for noisy speech recognition[J]. IEEE Signal Processing Letters,2006,13(11):711-714
[63]Jacquin A. E. Image coding based on a fractal theory of iterated contractive image transformations[J]. IEEE Transactions on Image Processing,1992,1(1): 18-30
[64]Koley C., Purkait P., Chakravorti S. SVM classifier for impulse fault identification in transformers using fractal features[J]. IEEE Transcations on Dielectrics and Electrical Insulation,2007,14(6):1538-1547
[65]Davis G. M. A wavelet-based analysis of fractal image compression[J]. IEEE Transactions on Image Processing,1998,7(2):141-154
[66]翟学明,张盼,朱永利,等.基于分形理论的绝缘子泄漏电流数据压缩方法[J].计算机工程与应用,2012,48(35)：224-227
[67]惠阿丽,林辉,贺博.用分形理论压缩和恢复高压绝缘子泄漏电流[J].高电压技术,2008,34(7)：1395-1400
[68]Satish L., Zaengl W. S. Can fractal features be used for recognition 3-d partial discharge patterns[J]. IEEE Transaction on Dielectrics and Electrical Insulation, 1995,2(3):352-359
[69]成永红,谢小军,陈玉,等.气体绝缘系统中典型缺陷的超宽频带放电信号的分形分析[J].中国电机工程学报,2004,24(8)：99-102
[70]Krivda A., Gulski E., Satish L., et al. The use of fractal features for recognition of 3-d discharge patterns[J]. IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5):889-892
[71]Lo T., Leung H., Litva J., et al. Fractal characterization of sea-scattered signals and detection of sea surface target[C]. IEE Proceedings F Radar and Signal Processing,1993,140(4):243-250
[72]Candela R., Mirelli G., Schifani R. PD recognition by means of statistical and fractal parameters and a neural network[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2000,7(1):87-94
[73]杜伯学,魏国忠.基于小波与分形理论的电力设备局部放电类型识别[J].电网技术,2006,30(13)：76-80
[74]高凯,谈克雄,李福祺,等.基于散点集分形特征的局部放电模式识别研究[J].中国电机工程学报,2002,22(5)：22-26
[75]Li Jian, Sun Caixin, Grzybowski S. Partial discharge image recognition influenced by fractal image compression[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(2):496-504
[76]Gu Fengchang, Chang Hongchan, Kuo Chengchien. Gas-insulated switchgear PD signal analysis based on hilbert-huang transform with fractal parameters enhancemen[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2013,20(4):1049-1055
[77]李新,成晓瑛,孟小红.电气设备局部放电灰度图象的矩特征提取研究[J].矿业安全与环保,2003,30(6)：17-18,31
[78]李剑,孙才新,廖瑞金,等.用于局部放电图象识别的统计特征研究[J].中国电机工程学报,2002,22(9)：104-107
[79]陈伟根,杜杰,凌云,等.基于能量-小波矩特征分析的油纸绝缘气隙放电过程划分[J].仪器仪表学报,2013,34(5)：1062-1069
[80]孙才新,李新,李俭,等.小波与分形理论的互补性及其在局部放电模式识别中的应用研究[J].中国电机工程学报,2001,21(12)：73-76
[81]Lalitha E. M., Satish L. Wavelet analysis for classification of multi-source PD patterns[J]. IEEE Transactions on Electrical Insulation,2000,7(1):40-47
[82]Evagorou D., Kyprianou A., Lewin P.L., et al. Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network[J]. IET Science, Measurement and Technology, 2010,4(3):177-192
[83]王国利,郑毅,沈嵩,等AGA-BP神经网络用于变压器超高频局部放电模式识别[J].电工电能新技术,2003,22(2)：6-9
[84]李延沐,袁鹏,牟磊,等.基于自适应神经模糊推理系统(ANFIS)的变压器超高频局部放电模式识别[J].电工电能新技术,2005,24(4)：30-33
[85]Li Yanqing, Lu Fangcheng, Xin Baoan, et al. A new method using ultrasonic for partial discharge pattern recognition[C].2002 International Conference on Power System Technology,2002,2:1004-1007
[86]阳国庆,郑殿春,孙学勇.基于小波神经网络局部放电模式识别方法的实验研究[J].哈尔滨理工大学学报,2005,10(5)：98-101
[87]Kim J. T., Choi W., Oh S. K., et al. Fuzzy-neural networks (FNNs) algorithm for partial discharge pattern recognition[C].2008 International Conference on High Voltage Engineering and Application, Chongqing, China,2008:621-625
[88]弓艳朋,刘有为,吴立远.采用分形和支持向量机的气体绝缘组合电器局部放电类型识别[J].电网技术,2011,35(3)：135-139
[89]Hao L., Lewin P. L. Partial discharge source discrimination using a support vector machine[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2010,17(1):189-197
[90]Sharkawy R. M., Mangoubi R. S., Abdel-Galil T. K., et al. SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements [J]. IEEE Transactions on Dielectrics and Electrical Insulation,2007,14(3):669-678
[91]任先文,薛雷,宋阳,等.基于分形特征的最小二乘支持向量机局部放电模式识别[J].电力系统保护与控制,2011,39(14)：143-147
[92]Romero E., Toppo D. Comparing support vector machines and feedforward neural networks with similar hidden-layer weights[J]. IEEE Transactions on Neural Networks,2007,18(3):959-963
[93]Fei Shengwei, Zhang Xiaobin. Fault diagnosis of power transformer based on support vector machine with genetic algorithm[J]. Expert Systems with Applications,2009,36(8):11352-11357
[94]胡运红.支持向量机的若干算法研究[D].山东：山东科技大学,2011
[95]杨丽君,廖瑞金,孙才‘新,等.矢量量化在局部放电模式识别中的应用[J].中国电机工程学报,2009,29(31)：122-127
[96]汪可,杨丽君,廖瑞金,等.基于离散隐式马尔科夫模型的局部放电模式识别[J].电工技术学报,2011,26(8)：205-212
[97]全玉生,马彦伟,何秋宇,等.基于模糊概率论的变压器局放信号模式识别法[J].电力系统自动化,2006,30(4)：71-74
[98]段青,赵建国,马艳.基于粒子群优化的组合核函数相关向量机负荷预测模型[J].电机与控制学报,2010,14(6)：33-38
[99]何创新,李彦明,刘成良,等.基于滑动平均与相关向量机的齿轮早期故障智能诊断[J].振动与冲击,2010,29(12)：89-92
[100]杨成福,章毅.相关向量机及在说话人识别应用中的研究电子[J].科技大学学报,2010,39(2)：311-315
[101]陶新民,徐晶,杜宝祥,等.基于相空间RVM的轴承故障检测方法[J].振动与冲击,2008,27(10)：6-10
[102]Demir B., Erturk S. Hyperspectral image classification using relevance vector machines[J]. IEEE Geoscience and Remote Sensing Letters,2007,4(4): 586-590
[103]Gholami B., Haddad W. M., Tannenbaum A. R. Relevance vector machine learning for neonate pain intensity assessment using digital imaging[J]. IEEE Transactions on Biomedical Engineering,2010,57(6):1457-1466
[104]陈庆国,王永红,高文胜,等.局部放电在线监测的数据分析及现场干扰抑制[J].高电压技术,2005,31(11)：10-12
[105]EL-Leithy S. Wavelet footprints based signal recovery technique for signal coding[C].2012 Japan-Egypt Conference on Electronics, Communications and Computers, Alexandria,2012:57-62
[106]Dragotti P. L., Vetterli M. Wavelet Footprints:Theory, Algorithms, and Applications[J]. IEEE Transactions on Signal Processing,2003,51(5): 1306-1322
[107]汤宝平,杨昌棋,谭善文,等.基于平移不变的小波去噪方法及应用[J].重庆大学学报,2002,25(3)：1-5
[108]袁静,何正嘉,王晓东,等.平移不变多小波相邻系数降噪方法及其在监测诊断中的应用[J].机械工程学报,2009,45(4)：155-160
[109]Donoho D. L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627
[110]唐炬,黄江岸,张晓星,等.局部放电在线监测中混频周期性窄带干扰的抑制[J].中国电机工程学报,2010,30(13)：121-127
[111]Grinsted A., Moore J. C., Jevrejeva S. Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics,2004(11):561-566
[112]孙卫国,程炳岩.交叉小波变换在区域气候分析中的应用[J].应用气象学报,2008,19(4)：480-487
[113]Ruessink B. G., Coco G., Ranasinghe R., et al. A cross-wavelet study of alongshore nonuniform nearshore sandbar behavior[C]. International Joint Conference on Neural Networks, Vancouver, BC,2006:4310-4317
[114]Kyprianou A., Staszewski W. J. On the cross-wavelet analysis of doffing oscillator[J]. Journal of Sound and Vibration,1999,228(1):199-210
[115]杨若文,曹杰,黄玮,等.太阳常数与太阳黑子数关系的交叉小波分析[J].科学通报,2009,54(7)：871-875
[116]张进,冯志鹏,卢文秀,等.交叉小波变换在水轮机非平稳信号分析中的应用[J].中国电机工程学报,2010,30(23)：84-89
[117]余丹丹,张韧,洪梅,等.基于交叉小波与小波相干的西太平洋副高与东亚夏季风系统的关联性分析[J].南京气象学院学报,2007,30(6)：755-769
[118]Rajamani P., Dey D., Chakravorti S. Classification of dynamic insulation failures in transformer winding during impulse test using cross-wavelet transform aided foraging algorithm [J]. IET Electric Power Applications,2010, 4(9):715-726
[119]Plett M. I. Transient detection with cross wavelet transforms and wavelet coherence[J]. IEEE Transactions on Signal Processing,2007,55(5):1605-1611
[120]Dey D., Chatterjee B., Chakravorti S., et al. Rough-granular approach for impulse fault classification of transformers using cross-wavelet transform[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(5): 1297-1304
[121]Banerjee S., Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification[J]. IEEE Transactions on Instrumentation and Measurement,2013:1-8
[122]Petrock A. M., Donnelly D. L., Rosenberg M. L. Quantifying cardio-pulmonary correlations using the cross wavelet transform:validating a correlative method [C].30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, British Columbia, Canada,2008:2940-2943
[123]陈涛,孙卫国,程炳岩.交叉小波变换在河南降水时频分析中的应用[J].南京气象学院学报,2002,25(5)：685-692
[124]Torrence C., Compo G. P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological Society,1998,79(1):61-79
[125]朱晓峰.协方差矩阵和相关系数矩阵下主成分分析比较研究[J].中国体育科技,2005,41(3)：134-136
[126]任江涛,黄焕宇,孙婧昊,等.基于相关性分析及遗传算法的高维数据特征选择[J].计算机应用,2006,26(6)：1403-1405
[127]Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy[J]. Journal of Machine Learning Research,2004:1205-1224
[128]Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2005,17(4):491-502
[129]何晓群.现代统计分析方法与应用[M].北京：中国人民大学出版社,2007
[130]李森,宋国兵,康小宁,等.基于时域下相关分析法的小电流接地故障选线[J].电力系统保护与控制,2008,36(13)：15-20
[131]陈晓红.数据降维的广义相关分析研究[D].南京：南京航空航天大学,2011
[132]Wang Jianji, Zheng Nanning. A novel fractal image compression scheme with block classification and sorting based on pearson's correlation coefficient[J]. IEEE Transactions on Image Processing,2013,22(9):3690-3702
[133]Neto A. M., Victorino A. C., Fantoni I., et al. Real-time collision risk estimation based on pearson's correlation coefficient[J]. IEEE Workshop on Robot Vision, 2013:40-45
[134]Neto A. M., Rittner L., Leite N., et al. Pearson's correlation coefficient for discarding redundant information in real time autonomous navigation system[C]. 16th IEEE International Conference on Control Applications, Singapore,2007: 426-431
[135]石敏,吴正国,徐袭.基于概率神经网络和双小波的电能质量扰动自动识别[J].电力自动化设备,2006,26(3)：5-8
[136]王柏生,倪一清,高赞明.用概率神经网络进行结构损伤位置识别[J].振动工程学报,2001,14(1)：60-64
[137]Gerbec D., Gasperic S., Smon I., et al. Allocation of the load profiles to consumers using probabilistic neural networks[J]. IEEE Transactions on Power Systems,2005,20(2):548-555
[138]Tripathy M., Maheshwari R. P., Verma H. K. Application of probabilistic neural network for differential relaying of power transformer[J]. IET Generation, Transmission & Distribution,2007,1(2):218-222
[139]孙斌,周云龙,向新星,等.基于经验模式分解和概率神经网络的气液两相流识别[J].中国电机工程学报,2007,27(17)：72-77
[140]贾勇,何正友,赵静.基于小波熵和概率神经网络的配电网电压暂降源识别方法[J].电网技术,2009,33(16)：63-69
[141]Mishra S., Bhende C. N., Panigrahi B. K. Detection and classification of power quality disturbances using S-transform and probabilistic neural network[J]. IEEE Transactions on Power Delivery,2008,23(1):280-287
[142]Tripathy M., Maheshwari R. P., Verma H. K. Power transformer differential protection based on optimal probabilistic neural network[J]. IEEE Transactions on Power Delivery,2010,25(1):102-112
[143]Simon T., Daphne K. Support vector machine active learning with applications to text classification[J]. Journal of Machine Learning Research,2001:45-66
[144]Schohn G., Cohn D. Less is more:Active Learning with Support Vector Machines[C]. Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, USA,2000:839-846
[145]Mitra P., Murthy C. A., Pal S. K. A probabilistic active support vector learning algorithm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):413-418
[146]Pasolli E., Melgani F.. Bazi Y. Support vector machine active learning through significance space construction[J]. IEEE Geoscience and Remote Sensing Letters,2011,8(3):431-435
[147]Patra S., Bruzzone L. A batch-mode active learning technique based on multiple uncertainty for SVM classifier[J]. IEEE Geoscience Remote Sensing Letters, 2012,9(3):497-501
[148]Martens D., Baesens B. B., Van Gestel T. Decompositional rule extraction from support vector machines by active learning[J]. IEEE Transactions on Knowledge and Data Engineering,2009,21(2):178-191
[149]张健沛,徐华.支持向量机(SVM)主动学习方法研究与应用[J].计算机应用,2004,24(1)：1-3
[150]Crawford M. M., Tuia D., Yang H. L. Active learning:any value for classification of remotely sensed data[J]. Proceedings of the IEEE,2013, 101(3):593-608
[151]Milgram J., Cheriet M., Sabourin R, et al. Estimating accurate multi-class probabilities with support vector machines[C]. Proceedings of International Joint Conference on Neural Networks, Montreal, Canada,2005:1906-1911
[152]段丹青,陈松乔,杨卫平.基于SVM主动学习的入侵检测系统[J].计算机工程,2007,33(1)：153-155,180
[153]陈建杰,叶智宣.多分类SVM主动学习及其在遥感图像分类中的应用[J].测绘科学,2009,34(4)：97-100
[154]Riccardi G., Hakkani-Tur D. Active learning:theory and applications to automatic speech recognition[J]. IEEE Transcations on Speech and Audio Processing,2005,13(4):504-511
[155]龙军,殷建平,祝恩,等.主动学习研究综述[J].计算机研究与发展,2008,45：300-304
[156]Zliobaite I., Bifet A., Pfahringer B., et al. Active learning with drifting streaming data[J]. IEEE Transactions on Networks and Learning Systems,2014, 25(1):27-39
[157]Gosselin P. H., Cord M. Active learning methods for interactive image retrieval [J]. IEEE Transactions on Image Processing,17(7):1200-1211
[158]Rajan S., Ghosh J., Crawford M. M. An active learning approach to hyperspectral data classification[J]. IEEE Transactions on Geoscience Remote Sensing,2008,46(4):1231-1242
[159]朱洪斌,蔡郁.基于主动学习支持向量机的文本分类[J].计算机工程与应用,2009,45(2)：134-136
[160]陈荣,曹永锋,孙洪.基于主动学习和半监督学习的多类图像分类[J].自动化学报,2011,37(8)：954-962
[161]Joshi A. J., Porikli F., Papanikolopoulos N. P. Scalable active learning for multiclass image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2259-2273
[162]方瑞明.支持向量机理论及其应用分析[M].北京：中国电力出版社,2007
[163]邓乃扬,田英杰.数据挖掘中的新方法：支持向量机[M].北京：科学出版社,2004
[164]Hsu C. W., Lin C. J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425
[165]Mathur A., Foody M. Multiclass and binary SVM classification:implications for training and classification users[J]. IEEE Geoscience and Remote Sensing Letters,2008,5(2):241-245
[166]Hsu C. W., Chang C. C., Lin C. J. A practical guide to support vector classification[R]. Taipei:National Taiwan University,2003
[167]王书舟.支持向量机方法及其应用研究[D].哈尔滨：哈尔滨工业大学,2009
[168]Patle A., Chouhan D. S. SVM kernel functions for classification[C].2013 International Conference on Advances in Technology and Engineering, Mumbai, 2013:1-9
[169]Lin H. T., Lin C. J., Weng R. C. A note on platt's probabilistic outputs for support vector machines[J]. Machine Learning,2007:267-276
[170]廖瑞金,杨丽君,孙才新,等.基于局部放电主成分因子向量的油纸绝缘老化状态统计分析[J].中国电机工程学报,2006,26(14)：114-119
[171]李岳,吕克洪.主成分分析在铁谱磨粒识别中的应用研究[J].国防科技大学学报,2004,26(1)：89-94
[172]王辉,王克奇.基于主分量分析木材纹理的特征提取[J].森林工程,2006,22(6)：14-16
[173]Lai K. X., Phung B. T., Blackburn T. R. Partial discharge analysis using PCA and SOM[C]. Power Tech, Lausanne,2007:2133-2138
[174]Hu J., Si J., Olson B. P., et al. Feature detection in motor cortical spikes by principal component analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2005,13(3):256-262
[175]Yao Haibo, Tian Lei. A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction[J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1469-1478
[176]Chin J. T., Suter D. Incremental kernel principal component analysis[J]. IEEE Transactions on Image Processing,2007,16(6):1662-1674
[177]Langley P., Bowers E.J., Murray A. Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features:application to ECG-derived respiration[J]. IEEE Transactions on Biomedical Engineering,2010,57(4): 821-829
[178]Hao L., Lewin P. L., Hunter J. A., et al. Discrimination of multiple PD sources using wavelet decomposition and principal component analysis [J]. IEEE Transactions on Dielectrics and Electrical Insulation,2011,18(5):1702-1711
[179]Burke D. J., O'Malley M. J. A study of principal component analysis applied to spatially distributed wind power[J]. IEEE Transactions on Power Systems,2011, 26(4):2084-2092
[180]Tzimiropoulos G., Mitianoudis N., Stathaki T. Robust recognition of planar shapes under affine transforms using principal component analysis[J]. IEEE Signal Processing Letters,2007,14(10):723-726
[181]Zeng An, Pan Dan, Zheng Qilun, et al. Knowledge acquisition based on rough set theory and principal component analysis [J]. IEEE Intelligent Systems,2006, 21(2):78-85
[182]Morales J. A., Orduna E. A. Patterns extraction for lightning transmission lines protection based on principal component analysis[J]. IEEE Latin America Transactions,2013,11(1):518-524
[183]Wang Manghui. Partial discharge pattern recognition of current transformers using an ENN[J]. IEEE Transactions on Power Delivery,2005,20(3): 1984-1990
[184]安学利,赵明浩,蒋东翔,等.基于支持向量机和多源信息的直驱风力发电机组故障诊断[J].电网技术,2011,35(4)：117-122
[185]郭创新,朱承治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13)：128-134
[186]Tipping M. E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research,2001,1:211-244
[187]尹金良,朱永利,俞国勤.相关向量机及其在变压器故障诊断中的应用[J].电力自动化设备,2012,32(8)：130-134
[188]Tipping M. E. The relevance vector machine[J]. Advances in Neural Information Processing Systems,2001(12):652-658
[189]周欣,吴瑛.基于SVM和RVM的信号调制分类研究[J].通信学报,2010,31(8)：94-99
[190]孙国强,卫志农,翟玮星.基于RVM与ARMA误差校正的短期风速预测[J].电工技术学报,2012,27(8)：187-193
[191]Damoulas T., Ying Y., Girolami M. A., et al. Inferring sparse kernel combinations and relevance vectors:an application to subcellular localization of proteins[C].7th International Conference on Machine Learning and Applications, San Diego, USA,2008:577-582
[192]Psorakis I., Damoulas T., Girolami M. A. Multiclass relevance vector machines: sparsity and accuracy[J]. IEEE Transactions on Neural Networks,2010,21(10): 1588-1598
[193]Damoulas T., Girolami M. A. Combining feature spaces for classification[J]. Pattern Recognition,2009,42(11):2671-2683
[194]Zhan Zhihui, Zhang Jun, Li Yun, et al. Orthogonal learning particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation,2011, 15(6):832-847
[195]Ko Chianan, Chang Yingpin, Wu Chiaju. A PSO method with nonlinear time-varying evolution for optimal design of harmonic filters[J]. IEEE Transactions on Power Systems,2009,24(1):437-444
[196]李宁.粒子群优化算法的理论分析与应用研究[D].武汉：华中科技大学,2006
[197]Kennedy J., Eberhart R. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995:1942-1948
[198]Valle Y. D., Venayagamoorthy G. K., Mohagheghi S., et al. Particle swarm optimization:basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation,2008,12(2):171-195
[199]Deepalaxmi R., Balaji M., Rajini V. Particle swarm optimization based selection of optimal polymeric blend[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2013,20(3):922-931
[200]陈建华,李先允,邓东华,等.粒子群优化算法在电力系统中的应用综述[J].继电器,2007,35(23)：77-84
[201]De Oca M. A. M., Stutzle T., Birattari M., et al. Frankenstein's PSO:a composite particle swarm optimization algorithm [J]. IEEE Transactions on Evolutionary Computation,2009,13(5):1120-1132
[2]王致杰,徐余法,刘三明,等.电力设备状态监测与故障诊断[M].上海：上海交通大学出版社,2012
[3]操郭奎,许维宗,阮国方.变压器运行维护与故障分析处理[M].北京：中国电力出版社,2008
[4]王梦云.110 kV及以上变压器事故与缺陷统计分析[J].供用电,2007,24(1)：1-5
[5]吴广宁.电气设备状态监测的理论与实践[M].北京：清华大学出版社,2005
[6]王晓莺.变压器故障与监测[M].北京：机械工业出版社,2004
[7]金卓睿.变压器局部放电超高频监测分形天线与最优小波去噪及信号识别研究[D].重庆：重庆大学,2008
[8]朱德恒,谈克雄.电绝缘诊断技术[M].北京：中国电力出版社,1998
[9]葛景滂,邱昌容.局部放电测量[M].北京：机械工业出版社,1984
[10]邱昌容,王乃庆.电工设备局部放电及测试技术[M].北京：机械工业出版社,1994
[11]Hettiwatte S. N., Wang Z. D., Crossley P. A. Investigation of propagation of partial discharges in power transformers and techniques for locating the discharge[J]. IEE Proceedings in Science, Measurement and Technology,2005, 152(1):25-30
[12]Okabe S., Ueta G, Wada H., et al. Partial discharge signal propagation characteristics inside the winding of oil-immersed power transformer using the three-winding transformer model in air[J]. IEEE Transactions on Dielectrics and Insulation,2011,18(6):2024-2031
[13]郭俊,吴广宁,张血琴,等.局部放电检测技术的现状和发展[J].电工技术学报,2005,20(2)：29-35
[14]Krivda A. Automated recognition of partial discharge[J]. IEEE Transactions on Dielectrics and Insulation,1995,2(5):796-821
[15]Dreuger F. H., Gulski E., Krivda A. Classification of partial discharge[J]. IEEE Transactions on Dielectrics and Insulation,1993,28(6):917-931
[16]Ma H., Chan J. C., Saha T. K., et al. Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources[J]. IEEE Transactions on Dielectrics and Insulation,2013,20(2):468-478
[17]GB/T 7354-2003,局部放电测量[S].中华人民共和国国家标准,2003
[1 8]桂峻峰,高文胜,谈克雄,等.脉冲电流法测量变压器局部放电的频带选择[J].高电压技术,2005,31(1)：45-46
[19]Breen H. J., Gulski E., Smit J. J., et al. Standardization of on-line VHF PD measurements on turbo generators[J]. IEEE Transactions on Dielectrics Electrical Insulation,2002,9(1):140-149
[20]Judd M. D., Yang L., Hunter I. B. B. Partial discharge monitoring of power transformers using UHF sensors Part I:sensors and signal interpretation[J]. IEEE Electrical Insulation Magazine,2005,21(2):5-14
[21]王国利,郑毅,郝艳捧.用于变压器局部放电检测的超高频传感器的初步研究[J].中国电机工程学报,2002,22(4)：154-160
[22]Judd M. D., Cleary G. P., Bennoch C. J. Applying UHF partial discharge detection to power transformers[J]. IEEE Power Engineering Review,2002, 22(8):57-59
[23]王晓宁,朱德恒.汽轮发电机局部放电在线监测技术的研究[J].大电机技术,2003(3)：1-4
[24]李燕青.超声波法检测电力变压器局部放电的研究[D].保定：华北电力大学,2004
[25]Kweon D. J., Chin S. B., Kwak H. R., et al. The analysis of ultrasonic signals by partial discharge and noise from the transformer[J]. IEEE transactions on Power Delivery,2005,20(3):1976-1983
[26]司文荣,李军浩,袁鹏,等.局部放电光测法的研究现状与发展[J].高压电器,2008,44(3)：261-264
[27]张晓星,任江波,故耀垓,等.SF6局部放电分解组分长光程红外检测[J].电工技术学报,2012,27(5)：70-76
[28]Gulski E. Digital analysis of partial discharges [J]. IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5):822-837
[29]梁流铭.变压器典型缺陷局部放电特性及与油中溶解气体对应关系研究[D].重庆：重庆大学,2009
[30]陈伟根,孙才新,廖瑞金,等.变压器局部放电与油中气体浓度的对应关系[J].重庆大学学报(自然科学版),2000,23(4)：86-89
[31]Muhamad N. A., Phung B. T., Blackburn T. R. Dissolved gas analysis (DGA) of partial discharge fault in bio-degradable transformer insulation oil[C]. Power Engineering Conference, Perth, Western Australia,2007:1-6
[32]唐炬,孙才新,宋胜利,等.局部放电信号中的白噪声和窄带干扰[J].高电压技术,2002,28(12)：8-10
[33]王立欣,诸定秋,蔡维铮.局部放电在线监测中基于小波变换的阈值消噪算法研究[J].电网技术,2003,27(4)：46-48,78
[34]Satish L., Nazneen B. Wavelet-based denoising of partial discharge signals buried in excessive noise and interference[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2003,10(2):354-367
[35]Yang L., Judd M. D., Bennoch C. J. Denoising UHF signal for PD detection in transformers based on wavelet technique[C].2004 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Boulder, CO, USA,2004: 166-169
[36]杨霁,李剑,王有元,等.变压器局部放电监测中的小波去噪方法[J].重庆大学学报,2004,27(10)：67-70
[37]孙才新,李新,杨永明.从白噪声中提取局部放电信号的小波变换方法研究[J].电工技术学报,1999,14(3)：47-50
[38]李天云,程思勇,李光.基于交叉验证的局部放电白噪声抑制新方法[J].电工技术学报,2008,23(1)：19-23
[39]Ma X., Zhou C., Kemp I. J. Automated wavelet selection and thresholding for PD detection[J]. IEEE Electrical Insulation Magazine,2002,18(2):37-47
[40]刘云鹏,律方成,李成榕,等.基于数学形态滤波器抑制局部放电窄带周期性干扰的研究[J].中国电机工程学报,2004,24(3)：169-173
[41]Gaouda A. M., El-Hag A., Abdel-Galil T. K., et al. On-line detection and measurement of partial discharge signals in a noisy environment[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(4):1162-1173
[42]孙才新,罗兵,杜林,等.变压器局部放电在线监测中抗电磁干扰的定向耦合差动平衡法的研究[J].中国电机工程学报,1998,18(5)：340-344
[43]Kopf U., Feser K. Rejection of narrow-band noise and repetitive pulse in on-site PD measurements [J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1995,2(6):1180-1191
[44]刘蕾,吴克伟,冯义,等.脉冲极性鉴别局部放电测试仪的研制[J].华东电力,2004,32(2)：7-9
[45]郭亮,黄宏新,成永红,等.嵌入式局部放电检测系统的数字滤波器设计[J].高电压技术,2007,33(8)：44-47,51
[46]杨霁.基于小波多尺度变换的局部放电去噪与识别方法研究[D].重庆：重庆大学,2004
[47]李剑,杨洋,程长奎,等.变压器局部放电监测逐层最优小波去噪算法[J]. 高电压技术,33(8)：56-60
[48]Li Jian, Cheng Changkui, Jiang Tianyan, et al. Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(2):543-549
[49]何明格,殷鹰,林丽君,等.基于小波迹和匹配追踪算法的超声波检测信号消噪[J].应用基础与工程科学学报,2011,19(2)：297-304
[50]Dragotti P. L., Vetterli M. Deconvolution with wavelet footprints for ill-posed inverse problems[C].2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA,2002:1257-1260
[51]Dragotti P. L., Vetterli M. Wavelet transform footprints:catching singularities for compression and denoising[C].2000 International Conference on Image Processing, Vancouver, BC, Canada,2000, (2):363-366
[52]Wei H. T., Jeoti V. A wavelet footprints-based compression scheme for ECG signals[C]. TENCON,2004:283-286
[53]胡文堂,高胜友,余绍峰,等.统计参数在变压器局部放电模式识别中的应用[J].高电压技术,2009,35(2)：277-281
[54]Gulski E., Kreuger F. H. Computer-aided recognition of discharge sources[J]. IEEE Transactions on Electrical Insulation,1992,27(1):82-92
[55]满玉岩,高文胜,高凯,等.发电机局部放电的统计特征识别[J].电工技术学报,2006,21(4)：41-45
[56]Mazroua A. A., Bartnikas R., Salama M. M. A. Discrimination between PD pulse shapes using different neural network paradigms[J]. IEEE Transaction on Electrical Insulation.1994,1(6):1119-1131
[57]Wang Zhenyuan, Zhu Deheng, Tan Kexiong, et al. PD monitor system for power generators[J]. IEEE Transaction on Electrical Insulation,1998, 5(6):850-856
[58]王国利,郝艳捧,李彦明.油中典型局部放电模型放电波形特征参数的提取[J].电网技术,2001,25(10)：34-37,46
[59]Cacciari ML, Contin A., Montanari G. C. Use of a mixed-weibull distribution for the identification of PD phenomena[J]. IEEE Transaction on Dielectrics and Electrical Insulation,1995,2(4):614-627
[60]Contin A., Montanari G. C., Ferraro C. PD source recognition by Weibull processing of pulse height distribution[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2000,7(1):48-58
[61]Sarker N., Chaudhuri B. B. An efficient differential box-counting approach to compute fractal dimension of image [J]. IEEE Transactions on Systems, Man and Cybernetics,1994,24(1):115-120
[62]Pitsikalis V., Maragos P. Filtered dynamics and fractal dimensions for noisy speech recognition[J]. IEEE Signal Processing Letters,2006,13(11):711-714
[63]Jacquin A. E. Image coding based on a fractal theory of iterated contractive image transformations[J]. IEEE Transactions on Image Processing,1992,1(1): 18-30
[64]Koley C., Purkait P., Chakravorti S. SVM classifier for impulse fault identification in transformers using fractal features[J]. IEEE Transcations on Dielectrics and Electrical Insulation,2007,14(6):1538-1547
[65]Davis G. M. A wavelet-based analysis of fractal image compression[J]. IEEE Transactions on Image Processing,1998,7(2):141-154
[66]翟学明,张盼,朱永利,等.基于分形理论的绝缘子泄漏电流数据压缩方法[J].计算机工程与应用,2012,48(35)：224-227
[67]惠阿丽,林辉,贺博.用分形理论压缩和恢复高压绝缘子泄漏电流[J].高电压技术,2008,34(7)：1395-1400
[68]Satish L., Zaengl W. S. Can fractal features be used for recognition 3-d partial discharge patterns[J]. IEEE Transaction on Dielectrics and Electrical Insulation, 1995,2(3):352-359
[69]成永红,谢小军,陈玉,等.气体绝缘系统中典型缺陷的超宽频带放电信号的分形分析[J].中国电机工程学报,2004,24(8)：99-102
[70]Krivda A., Gulski E., Satish L., et al. The use of fractal features for recognition of 3-d discharge patterns[J]. IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5):889-892
[71]Lo T., Leung H., Litva J., et al. Fractal characterization of sea-scattered signals and detection of sea surface target[C]. IEE Proceedings F Radar and Signal Processing,1993,140(4):243-250
[72]Candela R., Mirelli G., Schifani R. PD recognition by means of statistical and fractal parameters and a neural network[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2000,7(1):87-94
[73]杜伯学,魏国忠.基于小波与分形理论的电力设备局部放电类型识别[J].电网技术,2006,30(13)：76-80
[74]高凯,谈克雄,李福祺,等.基于散点集分形特征的局部放电模式识别研究[J].中国电机工程学报,2002,22(5)：22-26
[75]Li Jian, Sun Caixin, Grzybowski S. Partial discharge image recognition influenced by fractal image compression[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(2):496-504
[76]Gu Fengchang, Chang Hongchan, Kuo Chengchien. Gas-insulated switchgear PD signal analysis based on hilbert-huang transform with fractal parameters enhancemen[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2013,20(4):1049-1055
[77]李新,成晓瑛,孟小红.电气设备局部放电灰度图象的矩特征提取研究[J].矿业安全与环保,2003,30(6)：17-18,31
[78]李剑,孙才新,廖瑞金,等.用于局部放电图象识别的统计特征研究[J].中国电机工程学报,2002,22(9)：104-107
[79]陈伟根,杜杰,凌云,等.基于能量-小波矩特征分析的油纸绝缘气隙放电过程划分[J].仪器仪表学报,2013,34(5)：1062-1069
[80]孙才新,李新,李俭,等.小波与分形理论的互补性及其在局部放电模式识别中的应用研究[J].中国电机工程学报,2001,21(12)：73-76
[81]Lalitha E. M., Satish L. Wavelet analysis for classification of multi-source PD patterns[J]. IEEE Transactions on Electrical Insulation,2000,7(1):40-47
[82]Evagorou D., Kyprianou A., Lewin P.L., et al. Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network[J]. IET Science, Measurement and Technology, 2010,4(3):177-192
[83]王国利,郑毅,沈嵩,等AGA-BP神经网络用于变压器超高频局部放电模式识别[J].电工电能新技术,2003,22(2)：6-9
[84]李延沐,袁鹏,牟磊,等.基于自适应神经模糊推理系统(ANFIS)的变压器超高频局部放电模式识别[J].电工电能新技术,2005,24(4)：30-33
[85]Li Yanqing, Lu Fangcheng, Xin Baoan, et al. A new method using ultrasonic for partial discharge pattern recognition[C].2002 International Conference on Power System Technology,2002,2:1004-1007
[86]阳国庆,郑殿春,孙学勇.基于小波神经网络局部放电模式识别方法的实验研究[J].哈尔滨理工大学学报,2005,10(5)：98-101
[87]Kim J. T., Choi W., Oh S. K., et al. Fuzzy-neural networks (FNNs) algorithm for partial discharge pattern recognition[C].2008 International Conference on High Voltage Engineering and Application, Chongqing, China,2008:621-625
[88]弓艳朋,刘有为,吴立远.采用分形和支持向量机的气体绝缘组合电器局部放电类型识别[J].电网技术,2011,35(3)：135-139
[89]Hao L., Lewin P. L. Partial discharge source discrimination using a support vector machine[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2010,17(1):189-197
[90]Sharkawy R. M., Mangoubi R. S., Abdel-Galil T. K., et al. SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements [J]. IEEE Transactions on Dielectrics and Electrical Insulation,2007,14(3):669-678
[91]任先文,薛雷,宋阳,等.基于分形特征的最小二乘支持向量机局部放电模式识别[J].电力系统保护与控制,2011,39(14)：143-147
[92]Romero E., Toppo D. Comparing support vector machines and feedforward neural networks with similar hidden-layer weights[J]. IEEE Transactions on Neural Networks,2007,18(3):959-963
[93]Fei Shengwei, Zhang Xiaobin. Fault diagnosis of power transformer based on support vector machine with genetic algorithm[J]. Expert Systems with Applications,2009,36(8):11352-11357
[94]胡运红.支持向量机的若干算法研究[D].山东：山东科技大学,2011
[95]杨丽君,廖瑞金,孙才‘新,等.矢量量化在局部放电模式识别中的应用[J].中国电机工程学报,2009,29(31)：122-127
[96]汪可,杨丽君,廖瑞金,等.基于离散隐式马尔科夫模型的局部放电模式识别[J].电工技术学报,2011,26(8)：205-212
[97]全玉生,马彦伟,何秋宇,等.基于模糊概率论的变压器局放信号模式识别法[J].电力系统自动化,2006,30(4)：71-74
[98]段青,赵建国,马艳.基于粒子群优化的组合核函数相关向量机负荷预测模型[J].电机与控制学报,2010,14(6)：33-38
[99]何创新,李彦明,刘成良,等.基于滑动平均与相关向量机的齿轮早期故障智能诊断[J].振动与冲击,2010,29(12)：89-92
[100]杨成福,章毅.相关向量机及在说话人识别应用中的研究电子[J].科技大学学报,2010,39(2)：311-315
[101]陶新民,徐晶,杜宝祥,等.基于相空间RVM的轴承故障检测方法[J].振动与冲击,2008,27(10)：6-10
[102]Demir B., Erturk S. Hyperspectral image classification using relevance vector machines[J]. IEEE Geoscience and Remote Sensing Letters,2007,4(4): 586-590
[103]Gholami B., Haddad W. M., Tannenbaum A. R. Relevance vector machine learning for neonate pain intensity assessment using digital imaging[J]. IEEE Transactions on Biomedical Engineering,2010,57(6):1457-1466
[104]陈庆国,王永红,高文胜,等.局部放电在线监测的数据分析及现场干扰抑制[J].高电压技术,2005,31(11)：10-12
[105]EL-Leithy S. Wavelet footprints based signal recovery technique for signal coding[C].2012 Japan-Egypt Conference on Electronics, Communications and Computers, Alexandria,2012:57-62
[106]Dragotti P. L., Vetterli M. Wavelet Footprints:Theory, Algorithms, and Applications[J]. IEEE Transactions on Signal Processing,2003,51(5): 1306-1322
[107]汤宝平,杨昌棋,谭善文,等.基于平移不变的小波去噪方法及应用[J].重庆大学学报,2002,25(3)：1-5
[108]袁静,何正嘉,王晓东,等.平移不变多小波相邻系数降噪方法及其在监测诊断中的应用[J].机械工程学报,2009,45(4)：155-160
[109]Donoho D. L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627
[110]唐炬,黄江岸,张晓星,等.局部放电在线监测中混频周期性窄带干扰的抑制[J].中国电机工程学报,2010,30(13)：121-127
[111]Grinsted A., Moore J. C., Jevrejeva S. Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics,2004(11):561-566
[112]孙卫国,程炳岩.交叉小波变换在区域气候分析中的应用[J].应用气象学报,2008,19(4)：480-487
[113]Ruessink B. G., Coco G., Ranasinghe R., et al. A cross-wavelet study of alongshore nonuniform nearshore sandbar behavior[C]. International Joint Conference on Neural Networks, Vancouver, BC,2006:4310-4317
[114]Kyprianou A., Staszewski W. J. On the cross-wavelet analysis of doffing oscillator[J]. Journal of Sound and Vibration,1999,228(1):199-210
[115]杨若文,曹杰,黄玮,等.太阳常数与太阳黑子数关系的交叉小波分析[J].科学通报,2009,54(7)：871-875
[116]张进,冯志鹏,卢文秀,等.交叉小波变换在水轮机非平稳信号分析中的应用[J].中国电机工程学报,2010,30(23)：84-89
[117]余丹丹,张韧,洪梅,等.基于交叉小波与小波相干的西太平洋副高与东亚夏季风系统的关联性分析[J].南京气象学院学报,2007,30(6)：755-769
[118]Rajamani P., Dey D., Chakravorti S. Classification of dynamic insulation failures in transformer winding during impulse test using cross-wavelet transform aided foraging algorithm [J]. IET Electric Power Applications,2010, 4(9):715-726
[119]Plett M. I. Transient detection with cross wavelet transforms and wavelet coherence[J]. IEEE Transactions on Signal Processing,2007,55(5):1605-1611
[120]Dey D., Chatterjee B., Chakravorti S., et al. Rough-granular approach for impulse fault classification of transformers using cross-wavelet transform[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2008,15(5): 1297-1304
[121]Banerjee S., Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification[J]. IEEE Transactions on Instrumentation and Measurement,2013:1-8
[122]Petrock A. M., Donnelly D. L., Rosenberg M. L. Quantifying cardio-pulmonary correlations using the cross wavelet transform:validating a correlative method [C].30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, British Columbia, Canada,2008:2940-2943
[123]陈涛,孙卫国,程炳岩.交叉小波变换在河南降水时频分析中的应用[J].南京气象学院学报,2002,25(5)：685-692
[124]Torrence C., Compo G. P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological Society,1998,79(1):61-79
[125]朱晓峰.协方差矩阵和相关系数矩阵下主成分分析比较研究[J].中国体育科技,2005,41(3)：134-136
[126]任江涛,黄焕宇,孙婧昊,等.基于相关性分析及遗传算法的高维数据特征选择[J].计算机应用,2006,26(6)：1403-1405
[127]Yu L., Liu H. Efficient feature selection via analysis of relevance and redundancy[J]. Journal of Machine Learning Research,2004:1205-1224
[128]Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2005,17(4):491-502
[129]何晓群.现代统计分析方法与应用[M].北京：中国人民大学出版社,2007
[130]李森,宋国兵,康小宁,等.基于时域下相关分析法的小电流接地故障选线[J].电力系统保护与控制,2008,36(13)：15-20
[131]陈晓红.数据降维的广义相关分析研究[D].南京：南京航空航天大学,2011
[132]Wang Jianji, Zheng Nanning. A novel fractal image compression scheme with block classification and sorting based on pearson's correlation coefficient[J]. IEEE Transactions on Image Processing,2013,22(9):3690-3702
[133]Neto A. M., Victorino A. C., Fantoni I., et al. Real-time collision risk estimation based on pearson's correlation coefficient[J]. IEEE Workshop on Robot Vision, 2013:40-45
[134]Neto A. M., Rittner L., Leite N., et al. Pearson's correlation coefficient for discarding redundant information in real time autonomous navigation system[C]. 16th IEEE International Conference on Control Applications, Singapore,2007: 426-431
[135]石敏,吴正国,徐袭.基于概率神经网络和双小波的电能质量扰动自动识别[J].电力自动化设备,2006,26(3)：5-8
[136]王柏生,倪一清,高赞明.用概率神经网络进行结构损伤位置识别[J].振动工程学报,2001,14(1)：60-64
[137]Gerbec D., Gasperic S., Smon I., et al. Allocation of the load profiles to consumers using probabilistic neural networks[J]. IEEE Transactions on Power Systems,2005,20(2):548-555
[138]Tripathy M., Maheshwari R. P., Verma H. K. Application of probabilistic neural network for differential relaying of power transformer[J]. IET Generation, Transmission & Distribution,2007,1(2):218-222
[139]孙斌,周云龙,向新星,等.基于经验模式分解和概率神经网络的气液两相流识别[J].中国电机工程学报,2007,27(17)：72-77
[140]贾勇,何正友,赵静.基于小波熵和概率神经网络的配电网电压暂降源识别方法[J].电网技术,2009,33(16)：63-69
[141]Mishra S., Bhende C. N., Panigrahi B. K. Detection and classification of power quality disturbances using S-transform and probabilistic neural network[J]. IEEE Transactions on Power Delivery,2008,23(1):280-287
[142]Tripathy M., Maheshwari R. P., Verma H. K. Power transformer differential protection based on optimal probabilistic neural network[J]. IEEE Transactions on Power Delivery,2010,25(1):102-112
[143]Simon T., Daphne K. Support vector machine active learning with applications to text classification[J]. Journal of Machine Learning Research,2001:45-66
[144]Schohn G., Cohn D. Less is more:Active Learning with Support Vector Machines[C]. Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, USA,2000:839-846
[145]Mitra P., Murthy C. A., Pal S. K. A probabilistic active support vector learning algorithm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):413-418
[146]Pasolli E., Melgani F.. Bazi Y. Support vector machine active learning through significance space construction[J]. IEEE Geoscience and Remote Sensing Letters,2011,8(3):431-435
[147]Patra S., Bruzzone L. A batch-mode active learning technique based on multiple uncertainty for SVM classifier[J]. IEEE Geoscience Remote Sensing Letters, 2012,9(3):497-501
[148]Martens D., Baesens B. B., Van Gestel T. Decompositional rule extraction from support vector machines by active learning[J]. IEEE Transactions on Knowledge and Data Engineering,2009,21(2):178-191
[149]张健沛,徐华.支持向量机(SVM)主动学习方法研究与应用[J].计算机应用,2004,24(1)：1-3
[150]Crawford M. M., Tuia D., Yang H. L. Active learning:any value for classification of remotely sensed data[J]. Proceedings of the IEEE,2013, 101(3):593-608
[151]Milgram J., Cheriet M., Sabourin R, et al. Estimating accurate multi-class probabilities with support vector machines[C]. Proceedings of International Joint Conference on Neural Networks, Montreal, Canada,2005:1906-1911
[152]段丹青,陈松乔,杨卫平.基于SVM主动学习的入侵检测系统[J].计算机工程,2007,33(1)：153-155,180
[153]陈建杰,叶智宣.多分类SVM主动学习及其在遥感图像分类中的应用[J].测绘科学,2009,34(4)：97-100
[154]Riccardi G., Hakkani-Tur D. Active learning:theory and applications to automatic speech recognition[J]. IEEE Transcations on Speech and Audio Processing,2005,13(4):504-511
[155]龙军,殷建平,祝恩,等.主动学习研究综述[J].计算机研究与发展,2008,45：300-304
[156]Zliobaite I., Bifet A., Pfahringer B., et al. Active learning with drifting streaming data[J]. IEEE Transactions on Networks and Learning Systems,2014, 25(1):27-39
[157]Gosselin P. H., Cord M. Active learning methods for interactive image retrieval [J]. IEEE Transactions on Image Processing,17(7):1200-1211
[158]Rajan S., Ghosh J., Crawford M. M. An active learning approach to hyperspectral data classification[J]. IEEE Transactions on Geoscience Remote Sensing,2008,46(4):1231-1242
[159]朱洪斌,蔡郁.基于主动学习支持向量机的文本分类[J].计算机工程与应用,2009,45(2)：134-136
[160]陈荣,曹永锋,孙洪.基于主动学习和半监督学习的多类图像分类[J].自动化学报,2011,37(8)：954-962
[161]Joshi A. J., Porikli F., Papanikolopoulos N. P. Scalable active learning for multiclass image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2259-2273
[162]方瑞明.支持向量机理论及其应用分析[M].北京：中国电力出版社,2007
[163]邓乃扬,田英杰.数据挖掘中的新方法：支持向量机[M].北京：科学出版社,2004
[164]Hsu C. W., Lin C. J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425
[165]Mathur A., Foody M. Multiclass and binary SVM classification:implications for training and classification users[J]. IEEE Geoscience and Remote Sensing Letters,2008,5(2):241-245
[166]Hsu C. W., Chang C. C., Lin C. J. A practical guide to support vector classification[R]. Taipei:National Taiwan University,2003
[167]王书舟.支持向量机方法及其应用研究[D].哈尔滨：哈尔滨工业大学,2009
[168]Patle A., Chouhan D. S. SVM kernel functions for classification[C].2013 International Conference on Advances in Technology and Engineering, Mumbai, 2013:1-9
[169]Lin H. T., Lin C. J., Weng R. C. A note on platt's probabilistic outputs for support vector machines[J]. Machine Learning,2007:267-276
[170]廖瑞金,杨丽君,孙才新,等.基于局部放电主成分因子向量的油纸绝缘老化状态统计分析[J].中国电机工程学报,2006,26(14)：114-119
[171]李岳,吕克洪.主成分分析在铁谱磨粒识别中的应用研究[J].国防科技大学学报,2004,26(1)：89-94
[172]王辉,王克奇.基于主分量分析木材纹理的特征提取[J].森林工程,2006,22(6)：14-16
[173]Lai K. X., Phung B. T., Blackburn T. R. Partial discharge analysis using PCA and SOM[C]. Power Tech, Lausanne,2007:2133-2138
[174]Hu J., Si J., Olson B. P., et al. Feature detection in motor cortical spikes by principal component analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2005,13(3):256-262
[175]Yao Haibo, Tian Lei. A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction[J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1469-1478
[176]Chin J. T., Suter D. Incremental kernel principal component analysis[J]. IEEE Transactions on Image Processing,2007,16(6):1662-1674
[177]Langley P., Bowers E.J., Murray A. Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features:application to ECG-derived respiration[J]. IEEE Transactions on Biomedical Engineering,2010,57(4): 821-829
[178]Hao L., Lewin P. L., Hunter J. A., et al. Discrimination of multiple PD sources using wavelet decomposition and principal component analysis [J]. IEEE Transactions on Dielectrics and Electrical Insulation,2011,18(5):1702-1711
[179]Burke D. J., O'Malley M. J. A study of principal component analysis applied to spatially distributed wind power[J]. IEEE Transactions on Power Systems,2011, 26(4):2084-2092
[180]Tzimiropoulos G., Mitianoudis N., Stathaki T. Robust recognition of planar shapes under affine transforms using principal component analysis[J]. IEEE Signal Processing Letters,2007,14(10):723-726
[181]Zeng An, Pan Dan, Zheng Qilun, et al. Knowledge acquisition based on rough set theory and principal component analysis [J]. IEEE Intelligent Systems,2006, 21(2):78-85
[182]Morales J. A., Orduna E. A. Patterns extraction for lightning transmission lines protection based on principal component analysis[J]. IEEE Latin America Transactions,2013,11(1):518-524
[183]Wang Manghui. Partial discharge pattern recognition of current transformers using an ENN[J]. IEEE Transactions on Power Delivery,2005,20(3): 1984-1990
[184]安学利,赵明浩,蒋东翔,等.基于支持向量机和多源信息的直驱风力发电机组故障诊断[J].电网技术,2011,35(4)：117-122
[185]郭创新,朱承治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13)：128-134
[186]Tipping M. E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research,2001,1:211-244
[187]尹金良,朱永利,俞国勤.相关向量机及其在变压器故障诊断中的应用[J].电力自动化设备,2012,32(8)：130-134
[188]Tipping M. E. The relevance vector machine[J]. Advances in Neural Information Processing Systems,2001(12):652-658
[189]周欣,吴瑛.基于SVM和RVM的信号调制分类研究[J].通信学报,2010,31(8)：94-99
[190]孙国强,卫志农,翟玮星.基于RVM与ARMA误差校正的短期风速预测[J].电工技术学报,2012,27(8)：187-193
[191]Damoulas T., Ying Y., Girolami M. A., et al. Inferring sparse kernel combinations and relevance vectors:an application to subcellular localization of proteins[C].7th International Conference on Machine Learning and Applications, San Diego, USA,2008:577-582
[192]Psorakis I., Damoulas T., Girolami M. A. Multiclass relevance vector machines: sparsity and accuracy[J]. IEEE Transactions on Neural Networks,2010,21(10): 1588-1598
[193]Damoulas T., Girolami M. A. Combining feature spaces for classification[J]. Pattern Recognition,2009,42(11):2671-2683
[194]Zhan Zhihui, Zhang Jun, Li Yun, et al. Orthogonal learning particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation,2011, 15(6):832-847
[195]Ko Chianan, Chang Yingpin, Wu Chiaju. A PSO method with nonlinear time-varying evolution for optimal design of harmonic filters[J]. IEEE Transactions on Power Systems,2009,24(1):437-444
[196]李宁.粒子群优化算法的理论分析与应用研究[D].武汉：华中科技大学,2006
[197]Kennedy J., Eberhart R. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995:1942-1948
[198]Valle Y. D., Venayagamoorthy G. K., Mohagheghi S., et al. Particle swarm optimization:basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation,2008,12(2):171-195
[199]Deepalaxmi R., Balaji M., Rajini V. Particle swarm optimization based selection of optimal polymeric blend[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2013,20(3):922-931
[200]陈建华,李先允,邓东华,等.粒子群优化算法在电力系统中的应用综述[J].继电器,2007,35(23)：77-84
[201]De Oca M. A. M., Stutzle T., Birattari M., et al. Frankenstein's PSO:a composite particle swarm optimization algorithm [J]. IEEE Transactions on Evolutionary Computation,2009,13(5):1120-1132
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