电力系统电能质量的扰动检测与识别方法研究
摘要
电能作为清洁环保，经济高效，易于控制和转换的能源，广泛的应用到生产和生活的各个领域。电能质量的优劣不仅影响电能用户利益，同样会影响电网的安全运行，所以对电能质量问题的检测具有重要的意义。针对目前主要的电能质量问题进行分类，并具体分析了各类型的电能质量问题的发生原因及其危害。根据常见的暂态和稳态电能质量扰动问题的特点，建立扰动数学模型，并分析了目前电能质量扰动的检测方法和分类方法的研究现状。本文主要工作内容如下：
1）采用小波包变换对电能质量扰动信号进行分析，根据扰动信号的特点研究小波包变换的采样频率、小波基函数和分解层数等参数的选取，分析得到各扰动类型的小波包节点的归一化能量分布，提出对能量分布进行处理得到具有明显变化的新小波包能量分布的方法。在新的能量分布中可以看出，含谐波的扰动和暂态振荡扰动在对应节点上的能量分布较大，可以提取对应节点的新小波包节点能量作为表征这些扰动的特征向量，并通过仿真对利用小波包变换提取扰动特征向量的可行性进行研究。
2）首先，采用HHT变换对各种类型的电能质量扰动信号进行分析，利用分析结果中的瞬时频率对不同扰动类型发生的起止时刻进行估计，并根据瞬时幅值和边际谱提取扰动信号的幅值特征和频率特征；由于HHT变换后特征提取效果不明显，为寻求更有效的提取特征值的方法，又采用S变换对各种类型的电能
质量扰动信号进行分析，利用分析结果中的最高频率幅值变化对扰动发生的起止时刻进行估计，并利用基频幅值变化和时间幅值平方和均值变化提取扰动信号的幅值特征和频率特征，具体分析了S变换提取扰动特征向量的过程。之后，通过对比两种方法的扰动时刻估计和特征提取的效果可以看出，利用S 变换的扰动起止时刻定位较准确，对应特征变化明显，阈值选取方便，方法更易实现。
3）针对单一特征向量不能有效的表征所有的电能质量扰动信号的差异性，
分析了不同检测方法特征提取的特点，对电能质量扰动的多特征组合逻辑进行了研究。根据小波包能量分布中谐波信号的明显差异，以及S变换中的频率特征对频率特征明显的含谐波和暂态振荡进行分类，并利用S变换得到的其它幅值特征和奇异性特征进行后续的分类。研究了利用多个概率神经网络构造多特征组合的分类器实现扰动的分类的方法。仿真结果表明，在噪声强度为40dB 时平均分类准确度可达99.25%，噪声强度为20dB时平均分类准确度也可达88.38%。
关键词：
电能质量扰动；小波包变换；HHT变换；S变换；多特征组合；概率神经网络
Rearch on Disturbances Detection and Classification发声训练方法
学分制for Power Quality of Power System
Abstract
Electrical energy is applied to various fields of production and life,which is clean and environmental protection,and it is easy to be controlled and transformed. The power quality not only affects the interests of urs,but also affects the safe operation of power grid,therefore,the detection of power quality problem has important significance.The main power quality problems are classified,and the caus and hazards of various types of power quality problems are analyzed. According to the common characteristics of transient and steady-state power quality disturbance problem,the mathematical models of the disturbance are established and the rearch status of power quality disturbance detection methods and classification methods are analyzed.In this paper,the main work content is as follows: The Wavelet Packet Transform is ud to analyze the power quality disturbance signal,and the wavelet packet energy distribution is dispod to a new wavelet packet energy distribution with marketed,the sampling frequency,wavelet basis function and decomposition level of Wavelet Packet Transform are lected according to the characteristics of the disturbance signal.The energy distribution of harmonic disturbance and transient oscillation disturbance in corresponding nodes of new wavelet packet energy distribution is bigger than the other power quality disturbance problems,therefore,the energy of corresponding node in the new wavelet packet
can be extracted as characteristic vector of the disturbances,and the feasibility of this method which using wavelet packet transform to extract disturbance characteristic vector can be analyzed through the simulation.
The HHT transform is adopted to analyze various types of power quality disturbance signals,and the instantaneous frequency of HHT analysis result is ud to estimate start-stop moment of different kinds of disturbance,the amplitude and拔河比赛心得体会
frequency characteristics of disturbance signal can be extracted according to the instantaneous amplitude and marginal spectrum.However,the feature extraction effect of HHT transform is not obvious.In order to find a more suitable method,the S transformation is ud to analyze the various types of power quality disturbance signals.The change of highest frequency amplitude is ud to estimate the start-stop moment of disturbance,the amplitude and frequency characteristics of disturbance signals can be extracted from the fundamental frequency amplitude changes and time average amplitude sum of squares.By comparing the effect of the two methods,it can be en that the estimation accuracy of start-stop moment is higher when using S transformation,and the characteristic changes obviously,convenient for threshold value lect,and easier to implement.
For the single characteristic vector can’t reprent the differences of all the power quality disturbance signal effectively,the characteristics of different feature detecting methods are analyzed,and the multiple features’combined logic of the power quality disturbance is rearched in this paper.The signal with harmonic and transient shock can be classified according to the obvious differences of the harmonic signal in the wavelet packet energy distribution and the obvious frequency characteristic in S transform,and the other amplitude characteristics and singularity characteristics can be classified by using S transform.The multi-feature combination classifier is rearched by using multiple probabilistic neural network,and simulation results demonstrate that the average classification accuracy can reach 99.25%when the noi intensity is40dB,and the average classification accuracy can reach88.38%when the noi intensity is40dB.
Key words:
The power quality disturbance;Wavelet Packet Transform;Hilbert-Huang Transform; S transform;Multi-features combination;Probabilistic Neural Networks
目录
摘要............................................................................................................
.....................................................................III 第1章绪论. (1)
1.1电能质量研究背景及意义 (1)
1.2电能质量扰动问题概述 (2)
700种多肉植物图鉴1.2.1电能质量问题的分类 (2)
1.2.2电能质量扰动数学模型 (3)
1.3电能质量分析研究现状 (5)
1.3.1电能质量扰动的检测 (7)
1.3.2电能质量扰动的分类 (9)
1.4论文研究内容 (10)appreciate用法
第2章基于小波包变换的电能质量扰动特征提取 (12)
2.1小波变换理论 (12)
2.2小波包变换理论 (14)我们并肩而行
棉花图片大全2.3小波包变换电能质量扰动特征提取 (16)
2.3.1小波包变换参数的确定 (16)
2.3.2电能质量扰动信号特征提取 (17)
2.4本章小结 (21)
第3章基于HHT和S变换的扰动时刻定位和特征提取 (23)
3.1基于HHT变换的扰动定位和特征提取 (23)谧组词
3.1.1HHT变换基本原理 (23)
