基于高阶累积量的暂态电能质量扰动分类研究
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摘要
为提高暂态电能质量扰动分类识别的精度,提出了一种基于高阶累积量与支持向量机的暂态电能质量扰动信号的分类识别算法。该算法利用高阶累积量提取脉冲暂态与振荡暂态2类扰动的3阶与4阶统计特征,并选取各阶统计结果中的极大值个数、极小值个数以及最大值、最小值共8个特征量作为支持向量机的输入。利用Matlab产生仿真数据对此方法进行了仿真验证,结果表明,高阶累积量可以有效表征暂态扰动特征,且受噪声影响小;结合支持向量机可有效分类识别这2类暂态扰动,在训练样本为50组,核函数选择为线性核函数时,识别率可达到99%;当混合有其他扰动分量时,该方法也有效。
To improve the precision of classification and recognition of transient power quality disturbances,a new classification and recognition algorithm for transient power quality disturbance signals,which is based on high-order cumulants(HOC) and support vector machine(SVM),is proposed.In the proposed algorithm,the 3-order and 4-order impulse statistical characteristics of two kinds of disturbances,i.e.,the impulse transient and oscillation transient,are extracted from HOC,and eight characteristic quantities,i.e.,the number of maximum values,the number of minimum values,the maximum and the minimum in 3-order and 4-order statistical results respectively,are chosen as the input of SVM.The proposed algorithm is verified by Matlab to obtain simulation data.Simulation results show that the transient disturbance characteristics can be effectively characterized by HOC and the disturbance characteristics are less influenced by noises;combining with SVM the two kinds of transient disturbances can be effectively recognized and identified.When training samples are 50 groups and the linear kernel function is chosen as the kernel function,the recognition rate can reach 99%;when other disturbance components are mixed with,the proposed algorithm is effective too.
To improve the precision of classification and recognition of transient power quality disturbances,a new classification and recognition algorithm for transient power quality disturbance signals,which is based on high-order cumulants(HOC) and support vector machine(SVM),is proposed.In the proposed algorithm,the 3-order and 4-order impulse statistical characteristics of two kinds of disturbances,i.e.,the impulse transient and oscillation transient,are extracted from HOC,and eight characteristic quantities,i.e.,the number of maximum values,the number of minimum values,the maximum and the minimum in 3-order and 4-order statistical results respectively,are chosen as the input of SVM.The proposed algorithm is verified by Matlab to obtain simulation data.Simulation results show that the transient disturbance characteristics can be effectively characterized by HOC and the disturbance characteristics are less influenced by noises;combining with SVM the two kinds of transient disturbances can be effectively recognized and identified.When training samples are 50 groups and the linear kernel function is chosen as the kernel function,the recognition rate can reach 99%;when other disturbance components are mixed with,the proposed algorithm is effective too.
引文
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[15]杨健维,罗国敏,何正友.基于小波熵权和支持向量机的高压输电线路故障分类方法[J].电网技术,2007,31(23):22-26.Yang Jingwei,Luo Guomin,He Zhengyou.High voltage transmission line fault classification based on entropy weight of wavelet and support vector machines[J].Power System Technology,2007,31(23):22-26(in Chinese).
[16]王德意,杨卓,杨国清.基于负荷混沌特性和最小二乘支持向量机的短期负荷预测[J].电网技术,2008,32(7):66-71.Wang Dengyi,Yang Zhuo,Yang Guoqing.Short-term load forecasting based on chaotic characteristic of loads and least squares support vector machines[J].Power System Technology,2008,32(7):66-71(in Chinese).
[17]何正友,蔡玉梅,王志兵,等.电力暂态信号小波分析的后处理方法研究[J].电网技术,2005,29(21):46-51.He Zhengyou,Cai Yumei,Wang Zhibing,et al.A study on post-analysis methods of wavelet-transformed power system transient signals[J].Power System Technology,2005,29(21):46-51(in Chinese).
[18]张全明,刘会金.最小二乘支持向量机在电能质量扰动分类中的应用[J].中国电机工程学报,2008,28(1):106-110.Zhang Quanming,Liu Huijin.Application of LS-SVM in classification of power quality disturbances[J].Proceedings of the CSEE,2008,28(1):106-110(in Chinese).
[2]Stones J,Collinson A.Power quality[J].Power Engineering Journal,2001,15(2):58-64.
[3]戴永寿,郑德玲,魏磊,等.高阶统计量地震子波估计建模[J].石油地球物理勘探,2006,41(5):514-518.Dai Yongshou,Zheng Deling,Wei Lei,et al.Estimation of high-order statistic seismic wavelet[J].Oil Geophysical Prospecting,2006,41(5):514-518(in Chinese).
[4]徐耀华,郭英,王刚,等.基于幅度谱高阶统计量的鲁棒语音端点检测算法[J].数据采集与处理,2008,23(4):390-396.Xu Yaohua,Guo Ying,Wang Gang,et al.Robust algorithm for voice activity detection based on spectral high order statistics[J].Journal of Data Acquisition&Processing,2008,23(4):390-396(in Chinese).
[5]杨洪耕,秦东,张正书,等.用Laguerre多项式描述谐波随机求和问题[J].电网技术,2005,29(14):26-29.Yang Honggeng,Qin Dong,Zhang Zhengshu,et al.A Study on summation of harmonics with random phases angles based on laguerre polynomials[J].Power System Technology,2005,29(14):26-29(in Chinese).
[6]mer N G,Dogan G E.Power-quality event analysis using higher order cumulants and quadratic classifiers[J].IEEE Transactions on Power Delivery,2006,21(2):883-889.
[7]Juan J G,Antonio M M,Antolino G,et al.Higher-order characterization of power quality transients and their classification using competitive layers[J].Measurement,2009,42(3):478-484.
[8]IEEE Std.1159-1995 IEEE Recommended practice for monitoring electric power quality[S].
[9]张贤达.现代信号处理[M].北京:清华大学出版社,2002:267-269,281-282.
[10]Jayadeva K R,C S.Twin support vector machines for pattern classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[11]Lin W M,Wu C H,Lin C H,et al.Detection and classification of multiple power-quality disturbances with wavelet multiclass SVM[J].IEEE Transactions on Power Delivery,2008,23(4):2575-2582.
[12]潘峰,程浩忠,杨镜非,等.基于支持向量机的电力系统短期负荷预测[J].电网技术,2004,28(21):39-42.Pan Feng,Chen Haozhong,Yang Jingfei,et al.Power system short-term load forecasting based on support vector machines[J].Power System Technology,2004,28(21):39-42(in Chinese).
[13]郝文斌,李群湛,马庆安,等.基于支持向量机的变压器励磁涌流仿真实现[J].电网技术,2006,30(1):60-64.Hao Wenbin,Li Qunzhan,Ma Qingan,et al.Simulation study and algorithm implementation of transformer excitation inrush current based on support vector machine[J].Power System Technology,2006,30(1):60-64(in Chinese).
[14]杜树新,吴铁军.模式识别中的支持向量机方法[J].浙江大学学报:工学版,2003,37(5):521-527.Du Shuxin,Wu Tiejun.Support vector machines for pattern recognition[J].Journal of Zhejiang University:Engineering Science,2003,37(5):521-527(in Chinese).
[15]杨健维,罗国敏,何正友.基于小波熵权和支持向量机的高压输电线路故障分类方法[J].电网技术,2007,31(23):22-26.Yang Jingwei,Luo Guomin,He Zhengyou.High voltage transmission line fault classification based on entropy weight of wavelet and support vector machines[J].Power System Technology,2007,31(23):22-26(in Chinese).
[16]王德意,杨卓,杨国清.基于负荷混沌特性和最小二乘支持向量机的短期负荷预测[J].电网技术,2008,32(7):66-71.Wang Dengyi,Yang Zhuo,Yang Guoqing.Short-term load forecasting based on chaotic characteristic of loads and least squares support vector machines[J].Power System Technology,2008,32(7):66-71(in Chinese).
[17]何正友,蔡玉梅,王志兵,等.电力暂态信号小波分析的后处理方法研究[J].电网技术,2005,29(21):46-51.He Zhengyou,Cai Yumei,Wang Zhibing,et al.A study on post-analysis methods of wavelet-transformed power system transient signals[J].Power System Technology,2005,29(21):46-51(in Chinese).
[18]张全明,刘会金.最小二乘支持向量机在电能质量扰动分类中的应用[J].中国电机工程学报,2008,28(1):106-110.Zhang Quanming,Liu Huijin.Application of LS-SVM in classification of power quality disturbances[J].Proceedings of the CSEE,2008,28(1):106-110(in Chinese).
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