电能质量扰动的自动识别和时刻定位研究
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摘要
电能在当今生产和生活中扮演了越来越重要的角色。近年来,电网的负荷结构发生了重大变化,一方面大量的非线性、波动性、冲击性负荷涌入电力系统,提高了生产效率和生活质量,但也造成电网的污染,导致电能质量下降；另一方面,随着计算机技术和半导体技术的发展,以计算机和微处理器为核心的高度自动化和智能化的电子设备在工业生产中得到广泛应用,但这些设备对电能质量比较敏感,受到电能质量问题的影响也越来越严重,造成了重大经济损失。因此,电能质量问题受到了越来越多的关注,而电能质量检测、分析及控制技术成为电力系统领域的研究热点。
研究电能质量的最终目的在于解决电能质量对用户的影响。电能质量监测为最终解决电能质量问题提供可靠依据,构成了电能质量控制的基础。建立电能质量监测系统对电能质量扰动进行检测、评估和分类是十分必要的。实现电能质量监测的基础是电能质量指标的计算方法和分析方法。这些方法需要从其准确性、正确性和可行性来考察其有效性。电能质量问题从大类来说分为稳态电能质量问题和暂态电能质量问题,它们的检测、分析和评估方法有很大的差别。数字信号处理技术的发展为电能质量的检测与分析提供了大量的分析工具。稳态电能质量问题的检测、分析通常采用傅立叶变换的方法,并有快速傅立叶变换算法,技术上相对成熟。暂态电能质量问题的检测与分析较稳态电能质量复杂。短时电压扰动如电压暂降、暂升和中断是暂态电能质量问题中对用户影响最大的电能质量问题,因此研究短时电压扰动的检测与定位方法有重要意义。
短时电压扰动的检测和定位为判断电能质量扰动产生的原因提供依据,成为电能质量问题研究中的热点问题。短时电压扰动的检测和定位主要是依据电压信号在扰动发生时的奇异性。目前常用的方法有时域扰动检测法、神经网络扰动检测法、小波变换法、数学形态学法、TEO能量算子法和其Hilbert-Huang变换法等。但当采样信号的背景噪声较强时,将会给扰动信号奇异点的检测和定位带来误差甚至不能定位。如何从含有随机噪声的扰动信号中检测与定位扰动成为一个焦点问题,也是考核一个扰动检测与定位方法优劣的标志。
电能质量扰动分类为电能质量扰动的治理提供可靠依据,因此研究电能质量扰动的分类识别方法构成了研究热点。电能质量扰动的分类识别方法分为两部分,即信号特征向量的提取和分类器分类。提取出的信号特征向量一方面要能够代表信号的特征,另一方面要考虑尽量减小特征向量的大小。常用的特征向量提取方法主要有小波变换、S变换、HHT变换等方法及其衍生出来的一些方法。分类器构造采用人工智能技术,主要方法有：人工神经网络、支持向量机、模糊分类、专家系统、模糊专家系统等。
本文着重研究暂态电能质量问题的检测和定位方法、电能质量扰动的分类方法,并基于虚拟仪器技术开发综合电能质量监测系统。本文的主要研究内容和成果如下：
(1)提出了基于小波变换能量分布和神经网络的电能质量扰动的自动分类方法。首先对采样信号进行小波变换,然后利用小波变换系数计算该信号的能量分布向量并与标准信号的能量分布向量进行比较,从而得到能量分布差向量。将该能量分布差向量作为所提取的信号特征向量,用于信号分类器输入,经三层BP神经网络得到电能质量扰动的类型。在对神经网络进行训练和测试时,为使所采用的信号具有一定的代表性,一方面使扰动的发生时刻和持续时间是随机量,另一方面在信号中加入一定比例的白噪声,以获取该方法在噪声环境下的特性。仿真结果表明,该方法能够在一定噪声环境下对电能质量扰动信号有较高的识别率,且性能保持稳定,证明了所提方法的正确性。
(2)提出了基于信号自回归模型的短时电压扰动起止时刻定位方法。该方法利用自回归模型线性预测理论对各个采样点数据进行预测,并与实际的电压采样数据进行比较,由二者的残差曲线上对应于短时电压扰动发生的起止点上的突变来确定短时电压扰动的准确起止时刻。在处理过程中,将采样信号分成相互重叠的数据段,在每个数据段内计算自回归模型,并在预测段内数据时保持不变,但各数据段之间使用相同阶数的自回归模型。仿真结果表明所提的方法对短时电压扰动的起止时刻定位准确,对噪声干扰和谐波干扰具有较强的抑制作用。对由投运空载变压器引起的电能质量问题进行了动模实验研究,投运空载变压器引起的励磁涌流中含有较高含量的二次谐波,对电力系统影响较大；投运空载变压器能够引起母线电压暂降,并且其母线电压是一个逐步恢复的过程,持续时间较长。利用所提的方法对投运空载变压器引起的电压暂降波形进行了分析,对电压暂降的起点定位非常准确。
(3)提出了基于Hankel矩阵奇异值分解技术的短时电压扰动检测与定位方法。首先根据短时电压扰动的电压采样序列构造Hankel矩阵,并对该矩阵进行奇异值分解；基于该分解结果将原电压信号分解到多个分解层,得到扰动信号的一种线性分解,在某些分解层上电压扰动起点和终点表现为突变,根据这些突变点可以定位扰动起始和结束时刻。从原采样序列中,在起点两侧分别提取一个周波信号进行FFT运算以获取扰动发生前后的基波幅值,基于两者幅值关系可以确定扰动的类型,并计算出其指标。对短时电压信号、混合信号及实测信号进行仿真计算,结果表明所提方法的正确性和有效性。通过与小波变换方法在定位奇异点的集中性和适用性方面进行比较,其结果说明所提方法比小波变换法优越。该方法计算量适中,适合于在线分析。
(4)开发了基于虚拟仪器技术的综合电能质量监测系统。该系统分为终端机系统、数据服务器系统和通讯系统三部分。对系统每一部分进行详细的功能分析,给出了系统功能结构图和系统的流程。系统的软件在虚拟仪器平台LabVIEW下开发。终端机系统包括硬件和软件两部分,其硬件部分包括互感器、信号调理电路及数据采集卡和工控机等；软件部分包括数据采集控制模块、电能质量指标计算模块,电能质量数据存储模块及电能质量数据统计模块等。数据服务器系统的硬件采用通用计算机即可；软件系统是其主要部分,其软件主要完成电能质量数据的统计,根据用户的需要从数据库中提取数据并以友好方式显示出来或输出统计报告。由于电能质量监测的终端机通常安装在中心变、配电站处,一般有通讯通道可用,因此通讯系统的硬件方面未介绍；在软件方面,采用DataSocket技术设计了运行于终端机和数据服务器系统的数据通讯软件。在通讯系统设计中,为保证通讯系统的可靠性,采用通讯双方相互返回验证信息的方法。搭建了短时电压扰动实验电路系统,采用所提出的基于Hankel矩阵奇异值分解技术的电能质量扰动检测与定位方法编写了短时电压扰动的检测分析程序,并对该系统进行了测试,测试结果证明了该方法的正确性。
Electric power plays a key role in industrial production and daily life. In recent years, the load structure has varied greatly. A great deal of nonlinear loads, fluctuant loads and impactive loads have swarmed into power system. They have improved the productive efficiency and the living quality, but they have also polluted the power system and decreased the quality of power supply. On the other hand, with the development of computer science and semiconductor technology, automatic and intelligent equipments controlled by them are widely used in industry. These equipments are very sensitive to power quality disturbances. The loss caused by power quality is enormous. Therefore, power quality has received more and more attention in recent years. Research on the detection, analysis and control of power quality becomes a hotspot problem in power system realm.
The ultimate purpose of study on power quality is to decrease its effects on sensitive loads. Monitoring of power quality provides reliable evidences to solve the problem and it is the basis for power quality control. It is very essential to set up monitoring system to detect, evaluate and classify power quality. Power quality indices calculating methods and the analysis methods are the bassis for power quality monitoring. These methods should be checked by their accuracy, correctness and feasibility. There are two kinds of power quality:steady state power quality and transient power quality. There are many differences on detection, analysis and evaluation methods between them. The development of digital signal processing technology provides a great deal of methods to detect and analyze power quality problems. Fourier transform is often used for steady state power quality and it has FFT for fast calculation. But the methods used to detect and analyze transient power quality are complicated. Intantaneous voltage disturbances such as voltage sag, swell and interruption are transient power quality problems which has big effects to consumers. Reach on intantaneous voltage disturbances is significancy.
Detection and time-stamp for instantaneous power quality provide proofs to estimate the reasons of power quality disturbances. It becomes a hotspot in power quality research. Instantaneous power quality signal often has singularity at the start and the end. This characteristic is used to detect instantaneous power quality and can be used to make time-stamp. Detection and time-stamp methods often used are time-domain disturbance detection method, neural network, wavelet transform, Teager energy operator, mathematical morphology transform and Hilbert-Huang transform, etc. Singular point is hard to be detected if the original signal has noise. It is a key point to detect the singular point form a noisy signal. And it can be used to evaluate whether a detection method is good or not.
Correct classification of power quality can provide proofs to solve the problem. Therefore, it is important to study the methods of classifying power quality. The methods contain two aspects:the extraction of feature vector and the classifier. The feature vector should represent the original signal uniquely and its size should be as small as possible. The methods often used to extract feature vector are wavelet transform, S-transform, Hilbert-Huang transform and the ones derived from them. Classifier is designed based on artificial intelligence. Artificial neural network, support vector machine, fuzzy logic, expert system and fuzzy-expert system are often used to design classifier.
In this thesis, instantaneous power quality is the main object. Research is devoted on the detection and time-stamp of instantaneous power quality disturbances, the classification of power quality disturbances and the design of an integral power quality monitoring system. The main contributions of the dissertation are as following:
(1) An automatic classification method of power quality disturbances is proposed based on wavelet energy distribution and BP neural network. Wavelet is used to perform multi-resolution to the original signals. The energy to every level is calculated using wavelet coefficients and the energy distribution is got by combining the energies into a vector. Also, the energy distribution of a standard signal is calculated. The difference of the two energy distribution is used as the feature vector. A three-layer BP neural network is used as the classifier. The output of the BP neural network gives out the type of the disturbance. Signals used to train and simulate neural network should be representative. That is the start point and the duration of the power quality disturbances should be stochastic. Also, the signals should contain noise component to examine the validity of the proposed method. Simulation results indicate that the identification rate of the power quality disturbances is high under noisy conditions. The method proposed in this thesis is proved to be valid.
(2) A method to detect the transition points of instantaneous voltage disturbances based on auto-regressive model (AR model) is proposed. Every sampling data of the original signal is estimated by AR model. The residual sequence is got for every sampling point using the original value and the estimation. Transition points are then detected by allocating the time instants where residuals are prominent and they are the instant where the instantaneous voltage disturbance occur and end. So time-stamp can be made according to transition points. In the process of disposal, the data sequence is first subdivided into overlapping and fixed-size segments. Parameter estimation is applied to every data segment using AR model. The AR model keeps constantly during estimating the values in the segment, but it is time-varying between two data segments. The rank is stable during the whole estimation. Simulation results indicate that the proposed method can locate the transition points of instantaneous disturbances correctly and can get correctly result even when the original signal is polluted by noise and harmonic. Power quality caused by transformer energizing is studied through experiment. Transformer energizing can cause inrush current. The inrush current contains 2nd harmonic, which has effects to power system. Transformer energizing can also cause voltage sag in the bus and the continuance time is longer because of the voltage increasing gradually. Voltage sag caused by transformer energizing is solved by the proposed method. For the start point, the time-stamp is very exact.
(3) A detection and time-stamp method of instantaneous voltage disturbances based on Hankel matrix singular value decomposition is proposed. Firstly, Hankel matrix is constructed using the time series of voltage signal. Then the singular value decomposition is executed to the Hankel matrix. The decomposition signals are calculated by the decomposition results. The decomposition signals are linear superposition. In some decomposition signals, the transition points show prominence and the instant where the intantaneous voltage disturbance start and end can be got. So time-stamp can be made according to transition points. At each side of the start point in the original signal, one cycle signal is taken out to calculate the fundamental component by FFT. The type of the voltage disturbance can be got by the relationship between the fundamental components and the indices can also be calculated. Signals contain single disturbance, hybrid signals and real signals are used in simulation. The results show the correctness and efficiency of the proposed method. The applicability and concentricity of locating transition point is compared between wavelet and the proposed method, the result indicates that the proposed method is superior to wavelet. The computation rate is moderate and the proposed method is fit for online analysis.
(4) Integral power quality monitoring system is designed in this dissertation. The system contains three subsystems:terminal system, data server system and communication system. Functional analysis is made for every subsystem. Functional structures and flowcharts are given. Software is designed by LabVIEW. The terminal system contains two parts:hardware system and software system. The hardware system contains transformer, signal conditioning circuit, data acquisition card and industrial computer, etc. The software system contains data acquisition module, power quality indices calculation module, power quality data storage module and power quality statistics module, etc. Hardware of the data server system can use a general computer. The data server system's software is the main part. Its function is to perform the statistics of power quality data. It can display data friendly or give reports with the desire of users. The terminal system is usually set up in central substation where communication channels can be used. So the hardware of communication system is not introduced. Communication software running at the terminal system and data server system is design based on DataSocket. Information feedback is used between both sides to ensure the reliability of communication. Instantaneous voltage disturbances experiment system is set up. The method to detect instantaneous voltage disturbances based on Hankel matrix singular value decomposition is used in the program. Testing results indicate that the method is correct.
研究电能质量的最终目的在于解决电能质量对用户的影响。电能质量监测为最终解决电能质量问题提供可靠依据,构成了电能质量控制的基础。建立电能质量监测系统对电能质量扰动进行检测、评估和分类是十分必要的。实现电能质量监测的基础是电能质量指标的计算方法和分析方法。这些方法需要从其准确性、正确性和可行性来考察其有效性。电能质量问题从大类来说分为稳态电能质量问题和暂态电能质量问题,它们的检测、分析和评估方法有很大的差别。数字信号处理技术的发展为电能质量的检测与分析提供了大量的分析工具。稳态电能质量问题的检测、分析通常采用傅立叶变换的方法,并有快速傅立叶变换算法,技术上相对成熟。暂态电能质量问题的检测与分析较稳态电能质量复杂。短时电压扰动如电压暂降、暂升和中断是暂态电能质量问题中对用户影响最大的电能质量问题,因此研究短时电压扰动的检测与定位方法有重要意义。
短时电压扰动的检测和定位为判断电能质量扰动产生的原因提供依据,成为电能质量问题研究中的热点问题。短时电压扰动的检测和定位主要是依据电压信号在扰动发生时的奇异性。目前常用的方法有时域扰动检测法、神经网络扰动检测法、小波变换法、数学形态学法、TEO能量算子法和其Hilbert-Huang变换法等。但当采样信号的背景噪声较强时,将会给扰动信号奇异点的检测和定位带来误差甚至不能定位。如何从含有随机噪声的扰动信号中检测与定位扰动成为一个焦点问题,也是考核一个扰动检测与定位方法优劣的标志。
电能质量扰动分类为电能质量扰动的治理提供可靠依据,因此研究电能质量扰动的分类识别方法构成了研究热点。电能质量扰动的分类识别方法分为两部分,即信号特征向量的提取和分类器分类。提取出的信号特征向量一方面要能够代表信号的特征,另一方面要考虑尽量减小特征向量的大小。常用的特征向量提取方法主要有小波变换、S变换、HHT变换等方法及其衍生出来的一些方法。分类器构造采用人工智能技术,主要方法有：人工神经网络、支持向量机、模糊分类、专家系统、模糊专家系统等。
本文着重研究暂态电能质量问题的检测和定位方法、电能质量扰动的分类方法,并基于虚拟仪器技术开发综合电能质量监测系统。本文的主要研究内容和成果如下：
(1)提出了基于小波变换能量分布和神经网络的电能质量扰动的自动分类方法。首先对采样信号进行小波变换,然后利用小波变换系数计算该信号的能量分布向量并与标准信号的能量分布向量进行比较,从而得到能量分布差向量。将该能量分布差向量作为所提取的信号特征向量,用于信号分类器输入,经三层BP神经网络得到电能质量扰动的类型。在对神经网络进行训练和测试时,为使所采用的信号具有一定的代表性,一方面使扰动的发生时刻和持续时间是随机量,另一方面在信号中加入一定比例的白噪声,以获取该方法在噪声环境下的特性。仿真结果表明,该方法能够在一定噪声环境下对电能质量扰动信号有较高的识别率,且性能保持稳定,证明了所提方法的正确性。
(2)提出了基于信号自回归模型的短时电压扰动起止时刻定位方法。该方法利用自回归模型线性预测理论对各个采样点数据进行预测,并与实际的电压采样数据进行比较,由二者的残差曲线上对应于短时电压扰动发生的起止点上的突变来确定短时电压扰动的准确起止时刻。在处理过程中,将采样信号分成相互重叠的数据段,在每个数据段内计算自回归模型,并在预测段内数据时保持不变,但各数据段之间使用相同阶数的自回归模型。仿真结果表明所提的方法对短时电压扰动的起止时刻定位准确,对噪声干扰和谐波干扰具有较强的抑制作用。对由投运空载变压器引起的电能质量问题进行了动模实验研究,投运空载变压器引起的励磁涌流中含有较高含量的二次谐波,对电力系统影响较大；投运空载变压器能够引起母线电压暂降,并且其母线电压是一个逐步恢复的过程,持续时间较长。利用所提的方法对投运空载变压器引起的电压暂降波形进行了分析,对电压暂降的起点定位非常准确。
(3)提出了基于Hankel矩阵奇异值分解技术的短时电压扰动检测与定位方法。首先根据短时电压扰动的电压采样序列构造Hankel矩阵,并对该矩阵进行奇异值分解；基于该分解结果将原电压信号分解到多个分解层,得到扰动信号的一种线性分解,在某些分解层上电压扰动起点和终点表现为突变,根据这些突变点可以定位扰动起始和结束时刻。从原采样序列中,在起点两侧分别提取一个周波信号进行FFT运算以获取扰动发生前后的基波幅值,基于两者幅值关系可以确定扰动的类型,并计算出其指标。对短时电压信号、混合信号及实测信号进行仿真计算,结果表明所提方法的正确性和有效性。通过与小波变换方法在定位奇异点的集中性和适用性方面进行比较,其结果说明所提方法比小波变换法优越。该方法计算量适中,适合于在线分析。
(4)开发了基于虚拟仪器技术的综合电能质量监测系统。该系统分为终端机系统、数据服务器系统和通讯系统三部分。对系统每一部分进行详细的功能分析,给出了系统功能结构图和系统的流程。系统的软件在虚拟仪器平台LabVIEW下开发。终端机系统包括硬件和软件两部分,其硬件部分包括互感器、信号调理电路及数据采集卡和工控机等；软件部分包括数据采集控制模块、电能质量指标计算模块,电能质量数据存储模块及电能质量数据统计模块等。数据服务器系统的硬件采用通用计算机即可；软件系统是其主要部分,其软件主要完成电能质量数据的统计,根据用户的需要从数据库中提取数据并以友好方式显示出来或输出统计报告。由于电能质量监测的终端机通常安装在中心变、配电站处,一般有通讯通道可用,因此通讯系统的硬件方面未介绍；在软件方面,采用DataSocket技术设计了运行于终端机和数据服务器系统的数据通讯软件。在通讯系统设计中,为保证通讯系统的可靠性,采用通讯双方相互返回验证信息的方法。搭建了短时电压扰动实验电路系统,采用所提出的基于Hankel矩阵奇异值分解技术的电能质量扰动检测与定位方法编写了短时电压扰动的检测分析程序,并对该系统进行了测试,测试结果证明了该方法的正确性。
Electric power plays a key role in industrial production and daily life. In recent years, the load structure has varied greatly. A great deal of nonlinear loads, fluctuant loads and impactive loads have swarmed into power system. They have improved the productive efficiency and the living quality, but they have also polluted the power system and decreased the quality of power supply. On the other hand, with the development of computer science and semiconductor technology, automatic and intelligent equipments controlled by them are widely used in industry. These equipments are very sensitive to power quality disturbances. The loss caused by power quality is enormous. Therefore, power quality has received more and more attention in recent years. Research on the detection, analysis and control of power quality becomes a hotspot problem in power system realm.
The ultimate purpose of study on power quality is to decrease its effects on sensitive loads. Monitoring of power quality provides reliable evidences to solve the problem and it is the basis for power quality control. It is very essential to set up monitoring system to detect, evaluate and classify power quality. Power quality indices calculating methods and the analysis methods are the bassis for power quality monitoring. These methods should be checked by their accuracy, correctness and feasibility. There are two kinds of power quality:steady state power quality and transient power quality. There are many differences on detection, analysis and evaluation methods between them. The development of digital signal processing technology provides a great deal of methods to detect and analyze power quality problems. Fourier transform is often used for steady state power quality and it has FFT for fast calculation. But the methods used to detect and analyze transient power quality are complicated. Intantaneous voltage disturbances such as voltage sag, swell and interruption are transient power quality problems which has big effects to consumers. Reach on intantaneous voltage disturbances is significancy.
Detection and time-stamp for instantaneous power quality provide proofs to estimate the reasons of power quality disturbances. It becomes a hotspot in power quality research. Instantaneous power quality signal often has singularity at the start and the end. This characteristic is used to detect instantaneous power quality and can be used to make time-stamp. Detection and time-stamp methods often used are time-domain disturbance detection method, neural network, wavelet transform, Teager energy operator, mathematical morphology transform and Hilbert-Huang transform, etc. Singular point is hard to be detected if the original signal has noise. It is a key point to detect the singular point form a noisy signal. And it can be used to evaluate whether a detection method is good or not.
Correct classification of power quality can provide proofs to solve the problem. Therefore, it is important to study the methods of classifying power quality. The methods contain two aspects:the extraction of feature vector and the classifier. The feature vector should represent the original signal uniquely and its size should be as small as possible. The methods often used to extract feature vector are wavelet transform, S-transform, Hilbert-Huang transform and the ones derived from them. Classifier is designed based on artificial intelligence. Artificial neural network, support vector machine, fuzzy logic, expert system and fuzzy-expert system are often used to design classifier.
In this thesis, instantaneous power quality is the main object. Research is devoted on the detection and time-stamp of instantaneous power quality disturbances, the classification of power quality disturbances and the design of an integral power quality monitoring system. The main contributions of the dissertation are as following:
(1) An automatic classification method of power quality disturbances is proposed based on wavelet energy distribution and BP neural network. Wavelet is used to perform multi-resolution to the original signals. The energy to every level is calculated using wavelet coefficients and the energy distribution is got by combining the energies into a vector. Also, the energy distribution of a standard signal is calculated. The difference of the two energy distribution is used as the feature vector. A three-layer BP neural network is used as the classifier. The output of the BP neural network gives out the type of the disturbance. Signals used to train and simulate neural network should be representative. That is the start point and the duration of the power quality disturbances should be stochastic. Also, the signals should contain noise component to examine the validity of the proposed method. Simulation results indicate that the identification rate of the power quality disturbances is high under noisy conditions. The method proposed in this thesis is proved to be valid.
(2) A method to detect the transition points of instantaneous voltage disturbances based on auto-regressive model (AR model) is proposed. Every sampling data of the original signal is estimated by AR model. The residual sequence is got for every sampling point using the original value and the estimation. Transition points are then detected by allocating the time instants where residuals are prominent and they are the instant where the instantaneous voltage disturbance occur and end. So time-stamp can be made according to transition points. In the process of disposal, the data sequence is first subdivided into overlapping and fixed-size segments. Parameter estimation is applied to every data segment using AR model. The AR model keeps constantly during estimating the values in the segment, but it is time-varying between two data segments. The rank is stable during the whole estimation. Simulation results indicate that the proposed method can locate the transition points of instantaneous disturbances correctly and can get correctly result even when the original signal is polluted by noise and harmonic. Power quality caused by transformer energizing is studied through experiment. Transformer energizing can cause inrush current. The inrush current contains 2nd harmonic, which has effects to power system. Transformer energizing can also cause voltage sag in the bus and the continuance time is longer because of the voltage increasing gradually. Voltage sag caused by transformer energizing is solved by the proposed method. For the start point, the time-stamp is very exact.
(3) A detection and time-stamp method of instantaneous voltage disturbances based on Hankel matrix singular value decomposition is proposed. Firstly, Hankel matrix is constructed using the time series of voltage signal. Then the singular value decomposition is executed to the Hankel matrix. The decomposition signals are calculated by the decomposition results. The decomposition signals are linear superposition. In some decomposition signals, the transition points show prominence and the instant where the intantaneous voltage disturbance start and end can be got. So time-stamp can be made according to transition points. At each side of the start point in the original signal, one cycle signal is taken out to calculate the fundamental component by FFT. The type of the voltage disturbance can be got by the relationship between the fundamental components and the indices can also be calculated. Signals contain single disturbance, hybrid signals and real signals are used in simulation. The results show the correctness and efficiency of the proposed method. The applicability and concentricity of locating transition point is compared between wavelet and the proposed method, the result indicates that the proposed method is superior to wavelet. The computation rate is moderate and the proposed method is fit for online analysis.
(4) Integral power quality monitoring system is designed in this dissertation. The system contains three subsystems:terminal system, data server system and communication system. Functional analysis is made for every subsystem. Functional structures and flowcharts are given. Software is designed by LabVIEW. The terminal system contains two parts:hardware system and software system. The hardware system contains transformer, signal conditioning circuit, data acquisition card and industrial computer, etc. The software system contains data acquisition module, power quality indices calculation module, power quality data storage module and power quality statistics module, etc. Hardware of the data server system can use a general computer. The data server system's software is the main part. Its function is to perform the statistics of power quality data. It can display data friendly or give reports with the desire of users. The terminal system is usually set up in central substation where communication channels can be used. So the hardware of communication system is not introduced. Communication software running at the terminal system and data server system is design based on DataSocket. Information feedback is used between both sides to ensure the reliability of communication. Instantaneous voltage disturbances experiment system is set up. The method to detect instantaneous voltage disturbances based on Hankel matrix singular value decomposition is used in the program. Testing results indicate that the method is correct.
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