基于信号处理的电能质量扰动检测与识别
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摘要
现代电力系统负荷的改变使得电能质量问题日益突出,严重影响电力系统的稳定安全运行,并给生产和生活带来了巨大经济损失。在总结和分析前人科研成果的基础上,结合信号处理的相关理论,对电能质量扰动信号的消噪,检测和识别问题进行研究。
在介绍基于小波变换和数学形态学的消噪算法的基础上,研究这两类方法应用于电能质量扰动信号消噪的适应性和算法选择问题。建立了重构因子用以评价算法对信号的重构能力,利用两类方法的典型代表性算法对两种基本信号和常见的电能质量扰动信号进行了分析,通过计算其重构因子,讨论了两种算法对不同信号的适应性。通过仿真分析,得出如下结论：对于无暂态脉冲或高频振荡扰动的信号,两种算法都是有效的,可根据计算速度和精度的不同要求予以选择；对于含暂态脉冲和振荡扰动的信号,基于小波变换的消噪效果明显优于基于数学形态学的消噪方法。
利用数学形态学在保留信号突变点信息方面的良好效果,研究基于数学形态学的短时电能质量扰动检测和定位。介绍和分析了3种基于数学形态学的扰动检测和定位方法,即基于一阶求导和形态梯度的方法、基于形态梯度和软阈值处理的方法、基于dq分解和高帽变换的方法,通过仿真比较了3种方法在分析电压暂降、电压暂升、电磁暂态振荡等信号方面的适应性,能正确检测与定位出任意时刻发生的扰动,结果表明基于dq分解和高帽变换的方法在检测过零点扰动时具有很好的效果,因此选取基于dq分解和高帽变换的方法对实测扰动数据进行了检测和定位分析。
Cohen类双线性时频分布是一种真正意义上的时频联合分布,论文讨论了Cohen时频分布应用于电能质量扰动信号识别的可能性。给出一种基于重排二次型时频分布的电能质量检测新方法,首先采用瞬时无功功率理论和广义形态滤波器将电能质量信号的基波成分和扰动成分分离,再利用重排二次型时频分布对扰动分量进行分析,从而获得时频聚集性能更好的扰动分量的时频联合分布。仿真算例表明该方法直观的表达出扰动信号的时频特性,但在特征量化提取和降低计算量,解决交叉项问题方面还存在一定的困难。
提出了一种基于线性时频分布和二进制阈值特征矩阵的电能质量扰动分类方法。首先结合两种线性时频分布(窗口傅里叶变换和S变换)的优点,给出了提取能够表征信号特点的五个特征并对其进行二值化处理；在此基础上,建立了基于二进制阈值特征矩阵的扰动分类判据,通过将二进制特征与阈值矩阵进行数值比较来判定扰动的类型。对电压暂升,暂降,中断,切痕,振荡,脉冲,谐波,谐波和暂降,谐波和暂升9种常见扰动的仿真分析结果表明该方法有较高的准确率(>98%),说明所提出方法的正确性和有效性。
建立了一个分层的电能质量扰动识别系统,给出了部分特征并构造了系统中各个功能模块的具体算法。该分层电能质量扰动识别系统包括了基波和扰动分离模块,扰动时间特征提取和分类模块等七个功能模块,通过将dq变换,广义形态滤波,傅里叶变换等计算简单的信号分析方法相结合,逐层提取出幅值,扰动时间,扰动频域奇异熵等特征并进行分类,最后依据各层分类结果对信号的扰动类型进行综合识别。对七种常见的单一电能质量扰动及部分混合电能质量扰动仿真分析表明方法有较好的分类识别效果。
With the change of loads in power systems, the power quality problems are becoming increasingly prominent. Power quality problems not only endanger power system security and stability, but also bring vast economic loss. Based on the previous research, several key issues, such as power quality disturbance signal denoising, power system disturbance detection and identification are comprehensively and thoroughly studied and analyzed in this thesis.
Denoising based on wavelet transform and mathematical morphology have been proven to be effective, but whether they can work well when used in power disturbance signal denoising and how to choose the algorithm according to different signals and different accuracy requirements become a very important issue. A remodeling factor was established for evaluating the ability of algorithm in reconstructing signal. Through analyzing the simulation results of several typical power disturbance signals, calculated the remodeling factor then discussed the adaptability of different denosing algorithms used in different signals'denosing.. The result of the computer simulation indicates that for the signals without pulse or high frequency oscillation, disturbance both methods are effective, which should be chosen according to the evaluating speed and the different accuracy requirements.For the signal with pulse or high frequency oscillation disturbance, the denoising based on wavelet transform is Obviously better than the denoising based on mathematical morphology.
Because of the good effect on preserving abrupt signal, mathematical morphology(MM) often be used in detecting and locating short-time Power Quality Disturbance, but some methods based on mathematical morphology still has the shortcoming that some disturbances which crosses the zero spot couldn't be detected.. So this article has analyzed three method for detecting and locating power quality disturbance which based on Mathematical morphology, there are the method based on derivation and morphology gradient (MG),the method based on morphology gradient and soft threshold and the method based on Dq analysis and Top-hat transform. Compared adaptability of each method on the analysis of some common Power Quality Disturbances by emulating, finally found the method based on Dq analysis and Top-hat transform has good effect on detecting the disturbance which crosses the zero spot, so chose this method, analyzed some actual data. The result indicated that this method can detect and locate disturbances occurred at any time, proved it has good adaptability and feasibility.
Cohen distributions are joint time-frequency distributions in a real sense, learning the basic theory, a discussion was taken to study the possibility of power quality disturbance identification algorithm based on Cohen Bilinear Time-Frequency distributions in this thesis. A new method based on rearranged bilinear Time-Frequency distribution is introduced for power quality disturbance detection. First, separate fundamental component and disturbance with instantaneous reactive power theory and generalized morphological filter; then analyze the disturbance with arranged bilinear Time-Frequency distribution, accordingly for better time-frequency concentration. Simulation results of different PQ disturbances and crossed PQ disturbances have proven the effectiveness of the proposed method on the display of the time-frequency characteristics, but the quantitative feature extraction and the cross-term suppression problem are still unresolved.
Proposed a PQ disturbances classification method based linear time-frequency distribution (windowed Fourier transform and S-transform) and binary threshold feature matrix. Combined the advantages of WFD and ST, the method presents five features and binaries them, constitutes a binary threshold feature matrix, classifies different disturbances through comparing the magnitudes of the binary feature to the binary threshold feature matrix. Simulation results of 9 common kinds of disturbances indicate that the method has good performance of accuracy(>98%) and shows the validity and efficiency of the method.
A layered analysis systems was bulid for power quality identification, new features and algorithms were proposed to complete the function of the system which contains seven function modules. Using dq transform, generalized morphological filter, Fourier transform and others simple signal processing methods, these modules extracted amplitude disturbance time, disturbance frequency domain singular entropy and other features and classified them layer-by-layer, then comprehensive judgment was made for classification results of every layer. Simulation results of 6 common kinds of single disturbances and some mixed disturbances indicate that the method is effective.
在介绍基于小波变换和数学形态学的消噪算法的基础上,研究这两类方法应用于电能质量扰动信号消噪的适应性和算法选择问题。建立了重构因子用以评价算法对信号的重构能力,利用两类方法的典型代表性算法对两种基本信号和常见的电能质量扰动信号进行了分析,通过计算其重构因子,讨论了两种算法对不同信号的适应性。通过仿真分析,得出如下结论：对于无暂态脉冲或高频振荡扰动的信号,两种算法都是有效的,可根据计算速度和精度的不同要求予以选择；对于含暂态脉冲和振荡扰动的信号,基于小波变换的消噪效果明显优于基于数学形态学的消噪方法。
利用数学形态学在保留信号突变点信息方面的良好效果,研究基于数学形态学的短时电能质量扰动检测和定位。介绍和分析了3种基于数学形态学的扰动检测和定位方法,即基于一阶求导和形态梯度的方法、基于形态梯度和软阈值处理的方法、基于dq分解和高帽变换的方法,通过仿真比较了3种方法在分析电压暂降、电压暂升、电磁暂态振荡等信号方面的适应性,能正确检测与定位出任意时刻发生的扰动,结果表明基于dq分解和高帽变换的方法在检测过零点扰动时具有很好的效果,因此选取基于dq分解和高帽变换的方法对实测扰动数据进行了检测和定位分析。
Cohen类双线性时频分布是一种真正意义上的时频联合分布,论文讨论了Cohen时频分布应用于电能质量扰动信号识别的可能性。给出一种基于重排二次型时频分布的电能质量检测新方法,首先采用瞬时无功功率理论和广义形态滤波器将电能质量信号的基波成分和扰动成分分离,再利用重排二次型时频分布对扰动分量进行分析,从而获得时频聚集性能更好的扰动分量的时频联合分布。仿真算例表明该方法直观的表达出扰动信号的时频特性,但在特征量化提取和降低计算量,解决交叉项问题方面还存在一定的困难。
提出了一种基于线性时频分布和二进制阈值特征矩阵的电能质量扰动分类方法。首先结合两种线性时频分布(窗口傅里叶变换和S变换)的优点,给出了提取能够表征信号特点的五个特征并对其进行二值化处理；在此基础上,建立了基于二进制阈值特征矩阵的扰动分类判据,通过将二进制特征与阈值矩阵进行数值比较来判定扰动的类型。对电压暂升,暂降,中断,切痕,振荡,脉冲,谐波,谐波和暂降,谐波和暂升9种常见扰动的仿真分析结果表明该方法有较高的准确率(>98%),说明所提出方法的正确性和有效性。
建立了一个分层的电能质量扰动识别系统,给出了部分特征并构造了系统中各个功能模块的具体算法。该分层电能质量扰动识别系统包括了基波和扰动分离模块,扰动时间特征提取和分类模块等七个功能模块,通过将dq变换,广义形态滤波,傅里叶变换等计算简单的信号分析方法相结合,逐层提取出幅值,扰动时间,扰动频域奇异熵等特征并进行分类,最后依据各层分类结果对信号的扰动类型进行综合识别。对七种常见的单一电能质量扰动及部分混合电能质量扰动仿真分析表明方法有较好的分类识别效果。
With the change of loads in power systems, the power quality problems are becoming increasingly prominent. Power quality problems not only endanger power system security and stability, but also bring vast economic loss. Based on the previous research, several key issues, such as power quality disturbance signal denoising, power system disturbance detection and identification are comprehensively and thoroughly studied and analyzed in this thesis.
Denoising based on wavelet transform and mathematical morphology have been proven to be effective, but whether they can work well when used in power disturbance signal denoising and how to choose the algorithm according to different signals and different accuracy requirements become a very important issue. A remodeling factor was established for evaluating the ability of algorithm in reconstructing signal. Through analyzing the simulation results of several typical power disturbance signals, calculated the remodeling factor then discussed the adaptability of different denosing algorithms used in different signals'denosing.. The result of the computer simulation indicates that for the signals without pulse or high frequency oscillation, disturbance both methods are effective, which should be chosen according to the evaluating speed and the different accuracy requirements.For the signal with pulse or high frequency oscillation disturbance, the denoising based on wavelet transform is Obviously better than the denoising based on mathematical morphology.
Because of the good effect on preserving abrupt signal, mathematical morphology(MM) often be used in detecting and locating short-time Power Quality Disturbance, but some methods based on mathematical morphology still has the shortcoming that some disturbances which crosses the zero spot couldn't be detected.. So this article has analyzed three method for detecting and locating power quality disturbance which based on Mathematical morphology, there are the method based on derivation and morphology gradient (MG),the method based on morphology gradient and soft threshold and the method based on Dq analysis and Top-hat transform. Compared adaptability of each method on the analysis of some common Power Quality Disturbances by emulating, finally found the method based on Dq analysis and Top-hat transform has good effect on detecting the disturbance which crosses the zero spot, so chose this method, analyzed some actual data. The result indicated that this method can detect and locate disturbances occurred at any time, proved it has good adaptability and feasibility.
Cohen distributions are joint time-frequency distributions in a real sense, learning the basic theory, a discussion was taken to study the possibility of power quality disturbance identification algorithm based on Cohen Bilinear Time-Frequency distributions in this thesis. A new method based on rearranged bilinear Time-Frequency distribution is introduced for power quality disturbance detection. First, separate fundamental component and disturbance with instantaneous reactive power theory and generalized morphological filter; then analyze the disturbance with arranged bilinear Time-Frequency distribution, accordingly for better time-frequency concentration. Simulation results of different PQ disturbances and crossed PQ disturbances have proven the effectiveness of the proposed method on the display of the time-frequency characteristics, but the quantitative feature extraction and the cross-term suppression problem are still unresolved.
Proposed a PQ disturbances classification method based linear time-frequency distribution (windowed Fourier transform and S-transform) and binary threshold feature matrix. Combined the advantages of WFD and ST, the method presents five features and binaries them, constitutes a binary threshold feature matrix, classifies different disturbances through comparing the magnitudes of the binary feature to the binary threshold feature matrix. Simulation results of 9 common kinds of disturbances indicate that the method has good performance of accuracy(>98%) and shows the validity and efficiency of the method.
A layered analysis systems was bulid for power quality identification, new features and algorithms were proposed to complete the function of the system which contains seven function modules. Using dq transform, generalized morphological filter, Fourier transform and others simple signal processing methods, these modules extracted amplitude disturbance time, disturbance frequency domain singular entropy and other features and classified them layer-by-layer, then comprehensive judgment was made for classification results of every layer. Simulation results of 6 common kinds of single disturbances and some mixed disturbances indicate that the method is effective.
引文
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