电能质量扰动识别与分类的研究
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摘要
近年来,科学技术的不断创新和国民经济的蓬勃发展,使得对电能质量的要求越来越高。同时,由于电网干扰性负荷的日益增多,使得电能质量受到诸多方面的影响。电能质量问题现今已越来越严重,电压暂降和短时中断已被公认是影响许多用电设备正常安全运行最严重的问题。
对于稳态电能质量检测问题,国内外算法已经较为成熟,尤其采用高精度FFT算法对稳态问题的处理可以取得很高的精度。然而,对于暂态电能质量问题,无论是理论研究还是装置的开发都还处于尝试应用阶段。
采取合理的措施是提高电能质量的保证,首要的是应对其正确的检测和识别。基于这一点,本文对电能质量扰动(五种暂态电能质量扰动与谐波)检测和识别方法进行了研究,在总结检测方法的基础上提出了一种新的电能质量扰动识别与分类方法。
首先,较全面的介绍了电能质量有关概念、电能质量问题的分类,综述电能质量信号消噪与检测分析方法的现状,提出了电能质量扰动信号检测前的消噪处理的必要性,运用小波消噪方法对其进行消噪处理。
其次,本文提出了运用希尔伯特-黄变换方法和小波变换方法来对其扰动信号进行检测分析,用两章分别对暂态电能质量扰动(电压暂降、电压暂升、电压中断、暂态振荡和暂态脉冲)和谐波信号的检测进行了介绍与仿真。仿真结果表明,小波变换可以有效地区分暂态电能质量扰动和谐波信号,并能精确地定位暂态电能质量扰动的起止时间,但并不能区分暂态扰动类型,而希尔伯特-黄变换方法不仅能有效地区分暂态扰动类型,而且能够准确地检测其谐波信号。
最后,在总结上述两种方法(小波变换方法、希尔伯特-黄变换方法)对电能质量扰动信号进行检测结果的基础上,提出了基于小波变换和希尔伯特-黄变换的新的检测方法,详细地阐述了新检测方法的思想与检测步骤,并列举了三个仿真实例。仿真结果表明,该新方法的有效性与准确性。
In recent years, with the innovation of the science and technology and the rapid development of the national economy, the demand for power quality is becoming higher and higher. Meanwhile, the power quality is influenced by many factors as a result of the increased interference load in the power system. Nowadays, Power quality problems have been more and more serious; voltage sags and short interruptions have been recognized as the most serious problems affecting the safe operation of many electrical devices normally.
For the steady power quality detection, algorithms have been more mature at home and abroad, particularly it can be achieved very high precision by the high-precision FFT algorithm's handling of the steady. However, for the transient power quality problems, both theoretical research and the development of devices are still in the stage of trying to apply. Taking reasonable measures is the guarantee to improve the power quality, and the correct detection and identification of power quality are the first and foremost. For this reason, power quality disturbances (five transient power quality disturbances and harmonics) detection and identification methods are studied in this paper. On the basis of the conclusion of the detection methods, this paper presents a new recognition and classification method of power quality disturbances.
Firstly, a more comprehensive introduction to the concept of power quality, power quality classification and the present situation of power quality signals denoising and detection methods is proposed. This paper gives an illustration of the necessity of denoising before the power quality disturbances detection and uses wavelet-denoising method to denoise disturbances signal.
Secondly, this paper presents the use of Hilbert-Huang transform (HHT) and wavelet transform to detection analysis of the disturbances signal, with two chapters introducing and simulating the transient power quality disturbances (voltage sags, voltage swells, voltage interruption, transient oscillations and transient impulse) and harmonics signal detection. Simulation results show that wavelet transform can effectively distinguish transient power quality disturbances and harmonics, and can accurately locate the time of the transient power quality disturbances beginning and ending, but it does not distinguish types of transient disturbances, while HHT can not only distinguish types of transient disturbances, but also accurately detect harmonics.
Finally, on the basis of the conclusion of the results of power quality disturbances detection on these two methods (wavelet transform method, HHT method), a new detection method based on wavelet transform and HHT is proposed. This paper details the ideas and detection steps of this new method, and gives three simulation examples. Simulation results show the effectiveness and accuracy of this new method.
对于稳态电能质量检测问题,国内外算法已经较为成熟,尤其采用高精度FFT算法对稳态问题的处理可以取得很高的精度。然而,对于暂态电能质量问题,无论是理论研究还是装置的开发都还处于尝试应用阶段。
采取合理的措施是提高电能质量的保证,首要的是应对其正确的检测和识别。基于这一点,本文对电能质量扰动(五种暂态电能质量扰动与谐波)检测和识别方法进行了研究,在总结检测方法的基础上提出了一种新的电能质量扰动识别与分类方法。
首先,较全面的介绍了电能质量有关概念、电能质量问题的分类,综述电能质量信号消噪与检测分析方法的现状,提出了电能质量扰动信号检测前的消噪处理的必要性,运用小波消噪方法对其进行消噪处理。
其次,本文提出了运用希尔伯特-黄变换方法和小波变换方法来对其扰动信号进行检测分析,用两章分别对暂态电能质量扰动(电压暂降、电压暂升、电压中断、暂态振荡和暂态脉冲)和谐波信号的检测进行了介绍与仿真。仿真结果表明,小波变换可以有效地区分暂态电能质量扰动和谐波信号,并能精确地定位暂态电能质量扰动的起止时间,但并不能区分暂态扰动类型,而希尔伯特-黄变换方法不仅能有效地区分暂态扰动类型,而且能够准确地检测其谐波信号。
最后,在总结上述两种方法(小波变换方法、希尔伯特-黄变换方法)对电能质量扰动信号进行检测结果的基础上,提出了基于小波变换和希尔伯特-黄变换的新的检测方法,详细地阐述了新检测方法的思想与检测步骤,并列举了三个仿真实例。仿真结果表明,该新方法的有效性与准确性。
In recent years, with the innovation of the science and technology and the rapid development of the national economy, the demand for power quality is becoming higher and higher. Meanwhile, the power quality is influenced by many factors as a result of the increased interference load in the power system. Nowadays, Power quality problems have been more and more serious; voltage sags and short interruptions have been recognized as the most serious problems affecting the safe operation of many electrical devices normally.
For the steady power quality detection, algorithms have been more mature at home and abroad, particularly it can be achieved very high precision by the high-precision FFT algorithm's handling of the steady. However, for the transient power quality problems, both theoretical research and the development of devices are still in the stage of trying to apply. Taking reasonable measures is the guarantee to improve the power quality, and the correct detection and identification of power quality are the first and foremost. For this reason, power quality disturbances (five transient power quality disturbances and harmonics) detection and identification methods are studied in this paper. On the basis of the conclusion of the detection methods, this paper presents a new recognition and classification method of power quality disturbances.
Firstly, a more comprehensive introduction to the concept of power quality, power quality classification and the present situation of power quality signals denoising and detection methods is proposed. This paper gives an illustration of the necessity of denoising before the power quality disturbances detection and uses wavelet-denoising method to denoise disturbances signal.
Secondly, this paper presents the use of Hilbert-Huang transform (HHT) and wavelet transform to detection analysis of the disturbances signal, with two chapters introducing and simulating the transient power quality disturbances (voltage sags, voltage swells, voltage interruption, transient oscillations and transient impulse) and harmonics signal detection. Simulation results show that wavelet transform can effectively distinguish transient power quality disturbances and harmonics, and can accurately locate the time of the transient power quality disturbances beginning and ending, but it does not distinguish types of transient disturbances, while HHT can not only distinguish types of transient disturbances, but also accurately detect harmonics.
Finally, on the basis of the conclusion of the results of power quality disturbances detection on these two methods (wavelet transform method, HHT method), a new detection method based on wavelet transform and HHT is proposed. This paper details the ideas and detection steps of this new method, and gives three simulation examples. Simulation results show the effectiveness and accuracy of this new method.
引文
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[2]肖湘宁,徐永海,刘连光.供电系统电能质量[M].华北电力大学(北京), 2000年8月
[3]肖湘宁,韩民晓,徐永海.电能质量分析与控制[M].中国电力出版社, 2004年
[4] GB-12325-1990,电能质量供电电压允许偏差[S]
[5] GB/T15945-1995,电能质量电力系统频率允许偏差[S]
[6] GB/T14549-1993,电能质量公用电网谐波[S]
[7] GB-12326-2000,电能质量电压波动和闪变[S]
[8] GB/T15543-1995,电能质量三相电压允许不平衡度[S]
[9] GB/T-18481-2001,电能质量暂时过电压和瞬态过电压[S]
[10]胡铭,陈衍.电能质量及其分析方法综述[J].电网技术, 2000, 24(2):36-38
[11] Dugan R.C., Megranghan M.F., Benty H.W. Electrical power system quality[M]. New York: MC Graw-Hill, 1996
[12]张一工,谭伟璞,刘晋.电力系统电能质量[M].科学出版社, 2009年
[13]罗艳.电能质量扰动识别与定位[D].河北保定:华北电力大学, 2004年
[14]王洪磊.基于小波熵和SVM的动态电能质量扰动识别研究[D].河北保定:华北电力大学, 2007年
[15] Satish L., Gururaj B.I. Wavelet Analysis for Estimation of Mean-Curve of Impulse Waveforms Superimposed by Noise, Oscillation and Overshoot[J]. IEEE Transations on Powe Delivery, 2001, 16(1):116-121
[16]杨艳妮.小波多分辨率分析在信号去噪中的应用[J].热带农业工程, 2009, 33(1):57-60
[17]林渊,肖峰,郑宾,等.小波变换阈值降噪方法及在武器自动机数据处理中的应用[J].电子测量技术, 2009, 32(1):128-130
[18] Donoho D.L. De-noising by soft-thresheding[J]. IEEE Transaction on information theory, 1999, 12(41):617-627
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