Description: 一个毕业程序设计，是用小波和支持向量机对电能质量扰动的识别与分类，主要利用的能量特征。-A graduate program design is the use of wavelet and support vector machines for power quality disturbance recognition and classification, the main characteristics of energy use. Platform: |
Size: 2048 |
Author:张新新 |
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Description: 本文提出了一种自动检测和电能质量扰动分类模糊专家系统。有关干扰的类型包括电压骤降，骤升，中断，切换瞬变，冲动，闪烁，谐波和缺口。据推测，在所分析的波形采样的形式提供。傅立叶变换和小波分析，利用独特的功能获得的波形一个模糊专家系统为主体的决定有关的干扰类型而设计的。仿真研究，验证了该方法的比较研究人工神经网络为基础的分类方法和该方法研究的准确性也报告，以显示该方法的优点-This paper presents a fuzzy-expert system for automated detection and classification of power quality disturbances. The types of concerned disturbances include voltage sags, swells, interruptions, switching transients, impulses, flickers, harmonics, and notches. It is assumed that the analyzed waveforms are available in sampled form. Fourier transform and wavelet analysis are utilized to obtain unique features for the waveforms A fuzzy-expert system is designed for making a decision regarding the type of the disturbance. Simulation studies are presented to verify the accuracy of the proposed approach Comparison studies between an Artificial Neural Network based classification technique and the proposed approach are also reported to show the advantages of the proposed approach. Platform: |
Size: 444416 |
Author:韩小峰 |
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Description: The authors present an automatic classification of different power quality (PQ) disturbances using
wavelet packet transform (WPT) and fuzzy k-nearest neighbour (FkNN) based classifier. The training data
samples are generated using parametric models of the PQ disturbances. The features are extracted using
some of the statistical measures on the WPT coefficients of the disturbance signal when decomposed upto
the fourth level. These features are given to the fuzzy k-NN for effective classification. Platform: |
Size: 580608 |
Author:gk |
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Description: The Hilbert–Huang method is presented with modifications,
for time-frequency analysis of distorted power quality signals.
The empirical mode decomposition (EMD) is enhanced with
masking signals based on fast Fourier transform (FFT), for separating
frequencies that lie within an octave. Further, the instantaneous
frequency and amplitude of the constituent modes obtained
by Hilbert spectral analysis are improved by demodulation. The
method shows promising time-frequency-magnitude localization
capabilities for distorted power quality signals. The performance
of the new technique is compared with that of another multiresolution
analysis tool, the S-transform—a phase corrected wavelet
transform. Analysis on actual measurements of transformer inrush
current from an existing laboratory setup is used to demonstrate
this technique. Platform: |
Size: 835584 |
Author:imed |
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Description: 对基于小波变换的电能质量扰动检测和识别方法进行研究，在总结前人经验的基础上提出自己对算法的改进，并且利用MATLAB软件对改进算法进行仿真分析,取得令人满意的效果,验证所用方法的可行性。-Power quality disturbances detection and identification method based on wavelet transform and make their own improvements on the algorithm on the basis of previous experience, and improved algorithm for the simulation analysis using MATLAB software, obtain satisfactory results, verify thethe feasibility of the method. Platform: |
Size: 2172928 |
Author:车队 |
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Description: 电能质量检测平台仿真测试软件，用于验证各种检测算法的有效性，其中涉及小波变换，S变换，二叉树结构的支持向量机分类算法。-Power quality detection platform simulation test software used to verify the validity of the detection algorithm, which involves the wavelet transform, S transform and support vector machines for classification of the binary tree structure. Platform: |
Size: 30776320 |
Author:panfenghou |
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Description: 基于小波变换的信号奇异性检测和去噪Matlab编程，主要用于电能质量检测上。-Based on wavelet transform signal singularity detection and denoising Matlab programming, mainly for power quality detection. Platform: |
Size: 6144 |
Author:laura |
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Description: 小波变换对电能质量暂态信号的分析和运用，包含电压骤升、骤降等-Wavelet transform for power quality analysis and the use of transient signals, including voltage dips, swells, etc. Platform: |
Size: 10240 |
Author:yuyu |
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Description: 电压跌落是最严重的动态电能质量问题之一，
精确定位电压跌落起止时间是应对电压跌落问题的
重要前提和基础。由于电压采样信号往往有噪声分
量，现有的方法在定位电压跌落的起止时间时存在
局限性。本文提出利用多小波变换及相邻系数去噪
的电压跌落定位方法。多小波兼有对称性、正交性、
有限支撑性和二阶消失矩等优异的信号处理性能，
利用GHM多小波可以准确定位电压跌落起止时间。
多小波变换系数在每层之间具有对应关系，多小波
相邻系数将紧相邻的若干个系数作为一个整体来确
定阈值，考虑了系数之间的相关性，能获得更好的
去噪效果。通过 Matlab 进行仿真验证，仿真结果表
明，所提出的方法的正确性。
-Voltage sag is one of the most serious
dynamic power quality problems. Critical start-time
and end-time are important indices for voltage sags.
But the sampling signals often have noisy component,
the locations of start-time and end-time are hard to get.
Wavelet is an effective tool for those non-stationary
signal processing and has been used in this field. Local
feature in the signal can be enlarged after the
transformation using the scalar wavelet. But scalar
wavelets cannot contain orthogonality, symmetry,
compact support and higher order of vanishing
moments simultaneously. In this thesis, multi-wavelets
GHM is used to detect and locate power quality
disturbances. Multi-wavelets offer many excellent
properties such as the same approximation order but
more compact support. The dependence of the
multi-wavelets coefficients varies with the level, so
neighboring coefficien Platform: |
Size: 1363968 |
Author:李荣 |
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Description: In this paper, the expert system is introduced in
order to detect and classify commonly power quality
disturbances. This system is using learning vector quantization
artificial neural networks. Clustering method named fuzzy
c-mean is also utilized to initialize weight vector of first hidden
layer. It can mitigate the disadvantage of LVQ ANN. The
proposed system employs wavelet decomposition coefficients for
extracting of deviated signals features. The determined feature
vector is derived from Standard Deviation of 10-level
decomposition detail coefficients. For the purpose of having
efficient network, just 3 characteristic points among 10 points
have been used, that leads to make networks training much Platform: |
Size: 4096 |
Author:applepie12356 |
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Description: 为保障安全与电力用户供电质量，基于并网逆变器的分布式发电(distributed generation DG)系统要求具备孤岛检测功能。针对被动式孤岛检测法检测盲区(non-detectionzone，NDZ)大、检测时间长以及主动式孤岛检测法影响分布式发电系统供电质量的缺点，提出了一种新的被动式孤岛检测方法。该方法利用小波变换从公共耦合点(point ofcommon coupling，PCC)处的电压信号及逆变器输出电流信号中提取特征量，再通过BP 神经网络进行模式识别来判断是否出现孤岛现象。仿真与实验结果表明，该方法比传统的被动式孤岛检测方法检测速度快，检测盲区小。同时，由于所提供的孤岛检测法没有向控制信号中加入扰动量，因而不会对电能质量产生不良影响，克服了主动式孤岛检测方法的不足，并具有很高的准确性与可靠性。-The function of islanding detection is required for the grid-connected inverter-based distributed generation system due to safety reasons and to maintain the quality of power supply. Passive methods have a large non detection zoneand the detecting time is long, while active schemes have negative influence on power quality, so a novel passive islanding detection method was proposed. In this method, wavelet transform was adopted to extract feature vectors the voltage of point of common coupling (PCC) point and the output current of inverter, and then pattern recognition was exerted by BP neural network to determine whether there was an island phenomenon. The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection, and the non-detection zone is smaller. At the same time, because no disturbance was added to the control signal in the method, there isn’t a negative impact on power quality. The method overcomes the shortco Platform: |
Size: 609280 |
Author:kiel |
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Description: 为保障安全与电力用户供电质量，基于并网逆变器的分布式发电(distributed generation DG)系统要求具备孤岛检测功能。针对被动式孤岛检测法检测盲区(non-detectionzone，NDZ)大、检测时间长以及主动式孤岛检测法影响分布式发电系统供电质量的缺点，提出了一种新的被动式孤岛检测方法。该方法利用小波变换从公共耦合点(point ofcommon coupling，PCC)处的电压信号及逆变器输出电流信号中提取特征量，再通过BP 神经网络进行模式识别来判断是否出现孤岛现象。仿真与实验结果表明，该方法比传统的被动式孤岛检测方法检测速度快，检测盲区小。同时，由于所提供的孤岛检测法没有向控制信号中加入扰动量，因而不会对电能质量产生不良影响，克服了主动式孤岛检测方法的不足，并具有很高的准确性与可靠性。-The function of islanding detection is required for the grid-connected inverter-based distributed generation system due to safety reasons and to maintain the quality of power supply. Passive methods have a large non detection zoneand the detecting time is long, while active schemes have negative influence on power quality, so a novel passive islanding detection method was proposed. In this method, wavelet transform was adopted to extract feature vectors the voltage of point of common coupling (PCC) point and the output current of inverter, and then pattern recognition was exerted by BP neural network to determine whether there was an island phenomenon. The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection, and the non-detection zone is smaller. At the same time, because no disturbance was added to the control signal in the method, there isn’t a negative impact on power quality. The method overcomes the shortco Platform: |
Size: 609280 |
Author:kiel |
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Search - wavelet for power quality