基于FFT与形态加权动态时间规整的电压暂降源辨识新方法
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摘要
电压暂降源的准确辨识,有助于合理解决电压暂降问题和公正处理供用电纠纷。提出了一种基于快速傅里叶变换(FFT)与形态加权动态时间规整(SWDTW)的电压暂降源辨识新方法。首先分析了系统中可能出现的单一暂降源与复合暂降源所引起电压暂降波形特征。其次通过FFT方法分析信号谐波特征,构建并划分了特征波形库。然后针对传统的动态时间规整(DTW)方法中存在的不足之处,采用了一种基于形态特征的加权动态时间规整方法(SWDTW),用于对一维波形数据的相似度匹配。最后通过仿真证明,所述方法能有效实现电压暂降源的辨识,为电压暂降的有效治理以及相关纠纷问题的合理解决提供有力支持。
The accurate identification of voltage sag source helps to solve the problem of voltage sags and to fairly deal with the disputes of power supply. In this paper,a new identification method of voltage sag source is proposed,which combines fast Fourier transform( FFT) and a shape-based weighted dynamic time warping( SWDTW). Firstly,the waveform characteristics of voltage sags caused by single sags and composite sags are analyzed. Secondly,the characteristics of the signal harmonic are analyzed by FFT method,and the feature waveform library is constructed and divided. Then,in view of the shortcoming of traditional dynamic time warping( DTW) method,a shape-based weighted dynamic time warping( SWDTW) method is applied to match the one-dimensional waveform data. Finally,the simulation shows that the proposed method can effectively realize the identification of voltage sags and provide strong support for the effective control of voltage sags and the resolution of related disputes.
The accurate identification of voltage sag source helps to solve the problem of voltage sags and to fairly deal with the disputes of power supply. In this paper,a new identification method of voltage sag source is proposed,which combines fast Fourier transform( FFT) and a shape-based weighted dynamic time warping( SWDTW). Firstly,the waveform characteristics of voltage sags caused by single sags and composite sags are analyzed. Secondly,the characteristics of the signal harmonic are analyzed by FFT method,and the feature waveform library is constructed and divided. Then,in view of the shortcoming of traditional dynamic time warping( DTW) method,a shape-based weighted dynamic time warping( SWDTW) method is applied to match the one-dimensional waveform data. Finally,the simulation shows that the proposed method can effectively realize the identification of voltage sags and provide strong support for the effective control of voltage sags and the resolution of related disputes.
引文
[1]赵晨雪,陶顺,肖湘宁.基于电能质量检测系统电压暂降信息的故障定位估计方法[J].电网技术,2016,40(2):642-648.
[2]唐轶,陈嘉,樊新梅,等.基于扰动有功电流方向的电压暂降源定位方法[J].电工技术学报,2015,30(23):102-109.
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[12]邱海峰,陈兵,袁晓冬,等.基于动态时间弯曲距离的电压暂降源辨识方法[J].电力系统保护与控制,2017,45(13):7-13.
[13]储佳伟,袁晓冬,陈兵,等.结合小波分析和改进型DTW距离的配电网电压暂降源辨识方法[J].电网技术,2018,42(2):637-643.
[14]翟晓军,周波.一种改进的插值FFT谐波分析算法[J].中国电机工程学报,2016,36(11):2952-2958.
[15]王晓亮,李娜.基于加窗插值FFT算法的间谐波检测方法研究[J].现代电力,2012,29(5):28-31.
[16]林德清,顾伟,王元凯,等.基于动态时间弯曲空间距离度量的电能质量综合评估[J].电网技术,2013,37(2):562-567.
[17]黄纯,刘鹏辉,江亚群,等.基于动态时间弯曲距离的主动配电网馈线差动保护[J].电工技术学报,2017,32(6):240-247.
[18]JEONG Y S,JEONG M K,OMITAOMU O A.Weighted Dynamic Time Warping for Time Series Classification[J].Pattern Recognition,2011,44(9):2231-2240.
[19]叶燕清,杨克巍,姜江,等.基于加权动态时间弯曲的多元时间序列相似性匹配方法[J].模式识别与人工智能,2017,30(4):314-327.
[2]唐轶,陈嘉,樊新梅,等.基于扰动有功电流方向的电压暂降源定位方法[J].电工技术学报,2015,30(23):102-109.
[3]赵莹,赵川,叶华,等.应用主成分分析约简电压暂降扰动源识别特征的方法[J].电力系统保护与控制,2015,43(13):105-110.
[4]王克星,宋政湘,陈德桂,等.基于小波变换的配电网电压暂降的干扰源识别[J].中国电机工程学报,2003,23(6):29-34.
[5]周林,吴红春,孟婧,等.电压暂降分析方法研究[J].高电压技术,2008,34(5):1010-1016.
[6]丁宁,蔡维,锁娟,等.电压暂降源识别方法研究[J].电网技术,2008,32(2):55-59.
[7]赵凤展,杨仁刚.基于短时傅里叶变换的电压暂降扰动检测[J].中国电机工程学报,2007,27(10):28-34.
[8]祁博,邹金慧,范玉刚,等.基于Hilbert-Huang变换和小波能量谱的电压暂降源识别[J].中国电力,2013,46(8):112-117.
[9]杨洪耕,刘守亮,肖先勇.基于S变换的电压凹陷分类专家系统[J].中国电机工程学报,2007,27(1):98-104.
[10]陈丽,王硕,孔维功.基于改进S变换的复合电压暂降源识别特征分析[J].电力系统保护与控制,2014,42(4):27-33.
[11]李夏林,刘雅娟,朱武.基于配电网的复合电压暂降源分类与识别新方法[J].电力系统保护与控制,2017,45(2):131-139.
[12]邱海峰,陈兵,袁晓冬,等.基于动态时间弯曲距离的电压暂降源辨识方法[J].电力系统保护与控制,2017,45(13):7-13.
[13]储佳伟,袁晓冬,陈兵,等.结合小波分析和改进型DTW距离的配电网电压暂降源辨识方法[J].电网技术,2018,42(2):637-643.
[14]翟晓军,周波.一种改进的插值FFT谐波分析算法[J].中国电机工程学报,2016,36(11):2952-2958.
[15]王晓亮,李娜.基于加窗插值FFT算法的间谐波检测方法研究[J].现代电力,2012,29(5):28-31.
[16]林德清,顾伟,王元凯,等.基于动态时间弯曲空间距离度量的电能质量综合评估[J].电网技术,2013,37(2):562-567.
[17]黄纯,刘鹏辉,江亚群,等.基于动态时间弯曲距离的主动配电网馈线差动保护[J].电工技术学报,2017,32(6):240-247.
[18]JEONG Y S,JEONG M K,OMITAOMU O A.Weighted Dynamic Time Warping for Time Series Classification[J].Pattern Recognition,2011,44(9):2231-2240.
[19]叶燕清,杨克巍,姜江,等.基于加权动态时间弯曲的多元时间序列相似性匹配方法[J].模式识别与人工智能,2017,30(4):314-327.