Non-Stationary Harmonics Analysis Based on Wavelet Packet Transform and Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 1733-1737
Author(s):  
Wen Liang Zhao ◽  
Hong Song ◽  
Quan Pan ◽  
Ling Tang
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Frequency Spectrum ◽  
Wavelet Packet ◽  
Effective Means ◽  
Wavelet Packet Transform ◽  
Analysis Method ◽  
Software Simulation ◽  
Frequency Bands ◽  
Noise Interference

The method for analysis of stationary harmonics in power system is FFT, but it is unsuitable for non-stationary harmonics. Because of the feature that non-stationary harmonics’ frequency spectrum has a certain bandwidth and with some noise interference usually. A new method for detection, based on wavelet packet transform and neural network was presented in this paper. This method improved the traditional wavelet analysis method. The non-stationary harmonics were decomposed in different frequency bands by wavelet packet transform at first, and then complete the analysis of the non-stationary harmonic in different frequency bands. Through software simulation, the analysis results show that, the method has better accuracy, and provided an effective means for analyzing non-stationary harmonics.

scholarly journals An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2701 ◽  
Author(s):  
Masoud Ahmadipour ◽  
Hashim Hizam ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Neural Network ◽  
Shannon Entropy ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection Technique ◽  
Islanding Detection ◽  
Energy Entropy ◽  
Cost Parameters ◽  
Logarithmic Energy

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

Power Quality Disturbance Classification Using Wavelet Packet Transform and Back Propagation Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 7099-7109
Author(s):  
M. K Elango ◽  
A Jagadeesan ◽  
K. Mohana Sundaram
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Virtual Instrument ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Power Quality Disturbances

This paper develops a real time solution for detecting the Power Quality events. Fourteen events are generated through experimental setup and the signals are acquired through a voltage Data Acquisition Card, NI DAQ-9225, controlled by a Virtual Instrument software package. The features extracted from the Wavelet Transformation are fed into the Back Propagation Neural Network for training. By the virtue of a Neural Network property, it gets self-adapted and self-learned aiding in automatic classification of Power Quality Events. A combination of Wavelet Transform technique and Neural Networks are employed to detect and characterize the Power Quality Disturbances. The result obtained shows the effectiveness of the Wavelet Packet Transform based Back Propagation algorithm in classifying the Power Quality Disturbances. The results produced by the proposed methodology based Back Propagation Algorithm is verified with the Power Quality Analyser.

Fabric Weave Pattern’s Recognition Based on Texture Orientation Features

2013 ◽  
Vol 423-426 ◽  
pp. 2404-2408 ◽  
Author(s):  
Jian Qiang Shen ◽  
Ge Ge Li ◽  
Xuan Zou ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Wavelet Packet Transform ◽  
Original Image ◽  
Energy Distributions ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for representing fabric texture orientations and recognizing weave patterns. Wavelet packet transform is suited for fabric image decomposition in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet packet transform, and the regular orientations can be represented as the energy distributions of these images because the average energies of different fabric texture directions are changeable in a certain way. These energy orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify some complex weaves.

scholarly journals Detection and diagnosis of fault bearing using wavelet packet transform and neural network

2019 ◽  
Vol 13 (49) ◽  
pp. 291-301 ◽  
Author(s):  
Said DJABALLAH ◽  
Kamel Meftah ◽  
Khaled Khelil ◽  
Mohsein Tedjini ◽  
Lakhdar Sedira
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection And Diagnosis
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