scholarly journals Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

2019 ◽  
Vol 9 (11) ◽  
pp. 2228 ◽  
Author(s):  
Shiue-Der Lu ◽  
Hong-Wei Sian ◽  
Meng-Hui Wang ◽  
Rui-Min Liao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Discrete Wavelet ◽  
Correlation Degree ◽  
Power Quality Disturbance ◽  
Propagation Network ◽  
Parseval’S Theorem

The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.

Pembangunan Satu Kaedah Cekap dalam Pengesanan dan Pengkelasan Gangguan Kualiti Kuasa

2012 ◽  
Author(s):  
Ramizi Mohamed ◽  
Azah Mohamed ◽  
Aini Hussain
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Power Quality Disturbance ◽  
Voltage Swell ◽  
Power Quality Disturbances

Pengesanan dan pengkelasan data gangguan kualiti kuasa secara automatik telah menjadi penting terutamanya untuk menangani masalah gangguan pangkalan data yang besar. Kertas kerja ini membentangkan satu kaedah cekap dalam pengesanan dan pengkelasan gangguan kualiti kuasa. Kaedah yang dicadangkan untuk mengesan gangguan adalah berdasarkan penjelmaan anak gelombang diskrit dan pengekodan ramalan lelurus manakala kaedah yang telah dibangunkan untuk mengkelaskan gangguan adalah berdasarkan rangkaian neural tiruan (RNT). Sebelum pelaksaan RNT, isyarat gangguan dikesan terlebih dahulu untuk mendapatkan pekali anak gelombang kuasa dua dan pekali pengekodan ramalan lelurus. Pekali ini mewakili sifat bagi berbagai jenis gangguan dan digunakan sebagai data masukan kepada RNT yang telah dibina. Oleh itu, anak gelombang dan pengekodan ramalan lelurus digunakan sebagai prapemprosesan isyarat gangguan yang kemudiannya disambungkan kepada RNT. Dalam pelaksanan RNT, model rangkaian neural lapisan berbilang dengan algoritma perambatan ke belakang telah dipertimbangkan. Reka bentuk RNT yang telah dibangunkan adalah berbentuk hierarki dan modular supaya RNT yang berasingan dikhaskan untuk mengkelas berbagai jenis gangguan dan juga gangguan dengan kadar persampelan yang berbeza. Keputusan yang diperolehi menunjukkan bahawa kaedah anak gelombang dan pengekodan ramalan lelurus adalah sangat berkesan untuk mengesan gangguan kualiti kuasa dan kaedah RNT pula dapat mengkelaskan dengan jitu gangguan kualiti kuasa seperti lendut voltan, ampul voltan, fana dan takukan. Kata kunci: Kualiti kuasa; anak gelombang; pengekodan ramalan lelurus; rangkaian neural Automated power quality disturbance detection and classification is preferred so as to enable faster and more efficient analysis of a disturbance large database. This paper presents an efficient method to detect and classify some power quality disturbances. The proposed method for detecting the disturbances is based on discrete wavelet transform and linear predictive coding whereas the method for classifying the disturbances is based on artificial neural network (ANN). Prior to the ANN implementation, the disturbance signals are first detected by the discrete wavelet transform and the linear predictive coding techniques to obtain the squared wavelet transform coefficients and the linear predictive coding coefficients. These features represent the various disturbances and serve as inputs to the developed ANNs. Therefore, wavelets and linear predictive coding are employed as a preprocessing stage and is connected to the ANN. In the ANN implementation, the multilayer perceptron neural network model and the backpropagation algorithm are considered. The design of the developed ANNs are hierarchical as well as modular in nature so that separate ANNs are dedicated to classify the various types of disturbances and to handle the disturbances with different sampling rates. The results obtained show that the wavelets and the linear predictive coding methods are effective in detecting power quality disturbances and the ANNs can accurately classify the disturbances such as voltage sag, voltage swell, transients and notching. Key words: Power quality; wavelets; linear predictive coding; neural networks

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

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