基于小波变换和傅立叶变换的电能质量分析方法
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摘要
随着国民经济的迅速发展以及电力市场的逐步形成，电能质量问题在许多国家已经引起电力部门和用户的广泛关注，供电质量不高将引起产品质量下降，甚至导致某些重要的生产过程中断，从而造成严重的经济损失。
为了采取合理的措施提高电能质量，必须建立电能质量监测分析系统，对其进行正确地检测、评估和分类。本文从电能质量在线监测的角度出发，提出了一种基于小波变换和傅立叶变换的分析方法，对大量典型的电能质量信号用MATLAB软件进行了仿真分析，取得了令人满意的结果，验证了该方法的实用性和可行性。
实际的电能质量信号可能含有高频突变部分和噪声。小波变换的自适应性使其对电能质量扰动信号的消噪有着传统的低通滤波器无法比拟的优点。本文引入小波消噪方法对信号进行预处理，获得了良好的效果。用多分辨分析进行扰动的检测和提取是本文的重点。基于信号的奇异性检测理论，提出用Mallat分解算法第一层和第二层的高频系数作为区分稳态和非稳态现象的判据，进而确定扰动的持续时间。根据多分辨分析的频带划分原理，利用Mallat重构算法提取暂态扰动的波形。为了进一步区分短期变化扰动的类型，还编制了相应的扰动识别子程序，可以准确地判别电压骤降、电压骤升和断电等扰动。针对电力系统的稳态现象，本文利用快速傅立叶变换方法对谐波、闪变和纯正弦波电压等典型现象进行了分析，由傅立叶频谱作为区分谐波和闪变的一种手段。
本文首先系统地介绍了电能质量现象及其理论基础，对各种电能质量扰动的分类、定义、特征及其产生的原因进行了详细的阐述，并简要地介绍了我国
的电能质量标准以及电能质量监督管理体系；其次，对傅立叶变换、短时傅立
叶变换和小波变换这三种方法的基本原理及其在电能质量分析领域的应用现状
作了深入的探讨和比较，阐明了各种方法的特点及其适用条件；进而，对本文
提出的方法的原理及其实现作了详尽的说明；最后，以若干典型信号的仿真分
析为例验证了该方法的优越性。
With the rapid development of national economics and electricity markets,
power quality issues have captured considerable attention from both utility
companies and their customers in many countries, in part due to the proliferation of
sensitive electronic equipment. A poor supply quality can degrade the quality of
products, interrupt important industrial processes and therefore lead to economic
losses.
To improve the electric power quality, a monitoring and analyzing system must
be established to detect, estimate and classify different disturbances. As a
consequence, it must be supported by suitable measurement methods and apparatus.
The paper presents an on-line measurement method based on wavelet transform
and Fourier transform for power quality analysis in electric power systems. The
comparison of wavelet denoising with low-pass filtering is performed before the
wavelet is introduced as a pre-processing tool for power quality signals. The key of
this paper is to detect and extract disturbances with multi-resolution analysis (MRA).
The detection of the non-steady state disturbance and its duration are attained by a
proper application, on the sampled and denoised signal, of the Mallat decomposition
algorithm. And the modulus local maxima properties of the first-level and
second-level detail versions can be used as a criterion to classify steady and
non-steady state phenomena. The disturbance waveform is extracted by
reconstructing, in an optimized way, the signal in frequency subbands by means of
the MalIat reconstruction algorithm. To classify short-duration variations further, a
program is designed so that sags, swells and interruptions can be identified exactly.
As to steady state phenomena, the traditional signal analysis tool FFT is used for
harmonic analysis, which is also conceived for a means to distinguish harmonic and
flicker. The proposed method is characterized by its real-time property and it is
designed for an agile disturbance classification.
In the paper firstly the theoretical background of power quality phenomena is
expounded. Secondly, three transform-based methods (Fourier transform, short-time
Fourier transform and wavelet transform, respectively) and their applications are
discussed. Then, the proposed method is described in detail. Finally, some case
studies are examined in order to highlight the performance of the method.
为了采取合理的措施提高电能质量，必须建立电能质量监测分析系统，对其进行正确地检测、评估和分类。本文从电能质量在线监测的角度出发，提出了一种基于小波变换和傅立叶变换的分析方法，对大量典型的电能质量信号用MATLAB软件进行了仿真分析，取得了令人满意的结果，验证了该方法的实用性和可行性。
实际的电能质量信号可能含有高频突变部分和噪声。小波变换的自适应性使其对电能质量扰动信号的消噪有着传统的低通滤波器无法比拟的优点。本文引入小波消噪方法对信号进行预处理，获得了良好的效果。用多分辨分析进行扰动的检测和提取是本文的重点。基于信号的奇异性检测理论，提出用Mallat分解算法第一层和第二层的高频系数作为区分稳态和非稳态现象的判据，进而确定扰动的持续时间。根据多分辨分析的频带划分原理，利用Mallat重构算法提取暂态扰动的波形。为了进一步区分短期变化扰动的类型，还编制了相应的扰动识别子程序，可以准确地判别电压骤降、电压骤升和断电等扰动。针对电力系统的稳态现象，本文利用快速傅立叶变换方法对谐波、闪变和纯正弦波电压等典型现象进行了分析，由傅立叶频谱作为区分谐波和闪变的一种手段。
本文首先系统地介绍了电能质量现象及其理论基础，对各种电能质量扰动的分类、定义、特征及其产生的原因进行了详细的阐述，并简要地介绍了我国
的电能质量标准以及电能质量监督管理体系；其次，对傅立叶变换、短时傅立
叶变换和小波变换这三种方法的基本原理及其在电能质量分析领域的应用现状
作了深入的探讨和比较，阐明了各种方法的特点及其适用条件；进而，对本文
提出的方法的原理及其实现作了详尽的说明；最后，以若干典型信号的仿真分
析为例验证了该方法的优越性。
With the rapid development of national economics and electricity markets,
power quality issues have captured considerable attention from both utility
companies and their customers in many countries, in part due to the proliferation of
sensitive electronic equipment. A poor supply quality can degrade the quality of
products, interrupt important industrial processes and therefore lead to economic
losses.
To improve the electric power quality, a monitoring and analyzing system must
be established to detect, estimate and classify different disturbances. As a
consequence, it must be supported by suitable measurement methods and apparatus.
The paper presents an on-line measurement method based on wavelet transform
and Fourier transform for power quality analysis in electric power systems. The
comparison of wavelet denoising with low-pass filtering is performed before the
wavelet is introduced as a pre-processing tool for power quality signals. The key of
this paper is to detect and extract disturbances with multi-resolution analysis (MRA).
The detection of the non-steady state disturbance and its duration are attained by a
proper application, on the sampled and denoised signal, of the Mallat decomposition
algorithm. And the modulus local maxima properties of the first-level and
second-level detail versions can be used as a criterion to classify steady and
non-steady state phenomena. The disturbance waveform is extracted by
reconstructing, in an optimized way, the signal in frequency subbands by means of
the MalIat reconstruction algorithm. To classify short-duration variations further, a
program is designed so that sags, swells and interruptions can be identified exactly.
As to steady state phenomena, the traditional signal analysis tool FFT is used for
harmonic analysis, which is also conceived for a means to distinguish harmonic and
flicker. The proposed method is characterized by its real-time property and it is
designed for an agile disturbance classification.
In the paper firstly the theoretical background of power quality phenomena is
expounded. Secondly, three transform-based methods (Fourier transform, short-time
Fourier transform and wavelet transform, respectively) and their applications are
discussed. Then, the proposed method is described in detail. Finally, some case
studies are examined in order to highlight the performance of the method.
引文
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