Evaluation and classification of power quality disturbances based on discrete Wavelet Transform and artificial neural networks (original) (raw)
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CLASSIFICATION OF POWER QUALITY DISTURBANCES USING WAVELET AND NEURAL NETWORK
International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications, 2020
This paper presents a new approach to detect and classify power quality disturbances in power supply using wavelet Transform and neural network. The characteristic of the wavelet Transform has used for the analysis of Power Quality (PQ) disturbances under the noisy condition and has the ability to detect the disturbance correctly. The Power quality disturbances detection and classification are valuable tasks for protection of power system network. The features are extracted from the wavelet Transform output and it is trained by neural network for the classification of events. After training the classifier it is used to classify the PQ disturbances. Ten types of PQ disturbances are taken into account for the classification in this paper. The neural network has high classification accuracy, less calculation time and learning capability and reduction in complexity are improved. The simulation result shows that the combination of wavelet Transform and a neural network perform efficiently over existing methods in both signal detection and classification. The neural network will produce the exact output value of MSE, SSE and MSEReg for the neural network types. The best neural network is chosen manually from the extracted output.
Detection of power quality disturbances using wavelet transform and artificial neural network
2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 2014
This paper presents features that characterize power quality disturbances from recorded voltage waveforms using wavelet transform. The discrete wavelet transform has been used to detect and analyze power quality disturbances. The disturbances of interest include sag, swell, outage and transient. A power system network has been simulated by Electromagnetic Transients Program. Voltage waveforms at strategic points have been obtained for analysis, which includes different power quality disturbances. Then wavelet has been chosen to perform feature extraction. The outputs of the feature extraction are the wavelet coefficients representing the power quality disturbance signal. Wavelet coefficients at different levels reveal the time localizing information about the variation of the signal.
IJERT-Classification of Power Quality Disturbances using Wavelet Transform and Neural Network
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/classification-of-power-quality-disturbances-using-wavelet-transform-and-neural-network https://www.ijert.org/research/classification-of-power-quality-disturbances-using-wavelet-transform-and-neural-network-IJERTV4IS051331.pdf This Paper focuses on power quality event, detection and classification of power quality disturbances. The PQD detection and classification are valuable tasks for protection of power system network. In this work a new technique is used for categorizing PQ disturbances using MRA techniques of wavelet transform and neural network. These process having through three main components. First a simulator is used to generate power signal disturbances. The second component is a detector which uses the technique of DWT to detect the power signal disturbances. DWT is used to extract features in power signal. The third component is neural network architecture to classify the power signals disturbances with increased accuracy of classification.
Classification of power quality problems using wavelet based artificial neural network
2008
In this paper, a wavelet based artificial neural network classifier for recognizing power quality disturbances is implemented and tested. Discrete wavelet transforms based multi-resolution signal decomposition technique is integrated with the feed-forward neural network model to develop the power quality problem classifier. Classification of the power quality problems has been carried out in two parts. In first part, multi-resolution signal decomposition analysis with Parseval's energy theorem is used to extract the energy features of the power quality signal. In the second part, this feature information is used to develop neural network classifier. The classifier has been tested on various disturbances viz. voltage sag, swell, momentary interruption, capacitor switching and single line to ground fault. Results obtained show the versatility of the classifier for classifying the most commonly power quality problems.
Power Quality Disturbaces Clasification And Automatic Detection Using Wavelet And ANN Techniques
In this paper a development method to detect and classify the several power quality problems using the discrete wavelet transformation and artificial neural networks combined. There are several other methods in use to detect the same problem like Hilbert transform, Gabor transform, Gabor-Wigner transform, S transform, and Hilbert-Haung transform. The method of using wavelet and ANN includes the development of voltage waveforms of sampling rate and number of cycles, and also large number of power quality events with help of MATLAB software. The wavelet transformation and ANN tools used to get required coefficients. The obtained events of power quality monitored in each step to classify the particular event. These steps of the paper lead towards the automatic real time monitoring, detection and classification of power signals.
IET Conference Publications, 2009
This paper considers two important classification algorithms for to classify several power quality disturbances. Artificial Neural Network (ANN) and support vector machine (SVM). The last one is a novel algorithm that has shown good performance in general patterns classification. Nevertheless, Multilayer Perceptron Artificial Neural Network (MLPANN) is the most popular and most widely used models in various applications. Both are used for classify some disturbances under survey as: low frequency disturbances (such as flicker and harmonics) and high frequency disturbances (such as transient and sags). Biorthogonal Wavelet Function is used as a base function for extract features of PQ disturbances. In addition, RMS value is used to characterize the magnitude of disturbances.
2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, 2006
In this paper some patterns based on discrete Wavelet transform are studied for detection and identification of both, low frequency disturbances, like flicker and harmonics, and high frequency disturbances, such as transient and sags. Daubichies4 Wavelet function is used as a base function to detect and identify due to its frequency response and time localization information properties. Based on these patterns, power quality disturbances are automatically classified by support vector machines (SVM). Thus, Radial Base Function (RBF) was used as a kernel, because RBF requires only two parameters (σ and C) and cross validation technique and grid search were used in this work. SVM exhibit a good performance as classifier (90 percent of success for most disturbances) in spite of similitude between some disturbance patterns.
2016
In this paper, a new method for the detection and classification of all types of power quality disturbances is presented. In addition to separating the disturbance signals, the proposed method is able to determine the type of disturbances. Disturbance waveforms are generated based on IEEE 1159 standard and they are de-noised using discrete wavelet transform. To detect the sinusoidal signals from disturbance signals, new criteria have been proposed. By introducing these new criteria, the classification algorithm is not active for non-disturbance signals. Therefore, the computation time is reduced. If a signal has disturbance, to extract the required information, it is analyzed using discrete wavelet transform. Using this information, the appropriate feature vectors are introduced. Parallel neural networks structures are proposed for the classification of disturbances. The inputs of these networks are the introduced feature vectors. The proposed method is done for all power quality di...
Classification of Power Signal Disturbances Using Wavelet Based Neural Network
The power signal disturbances are classified as impulse, notches, glitches, momentary interruption, voltage sag/swell, harmonic distortion and flicker. These disturbances may cause malfunctioning of the equipments. To improve the quality of the power supply detection of the disturbance must be done accurately. In this paper DWT is employed to capture the time of transient occurrence and extract frequency features of power disturbances. These DWT coefficients when applied as inputs to the neural networks require large memory space and much learning time. Hence along with the Multi Resolution Analysis (MRA) technique the statistical methods are used to extract the disturbance features of the distorted signal at different resolution levels. For neural network structure Probabilistic Neural Network (PNN) and Feed Forward Back Propagation Network (FFBPN) are used to classify the disturbance type and are compared. The learning efficiency of PNN is very fast when compared to FFBPN, and it is suitable for signal classification problems. Distorted signals were generated by the power system block set in MATLAB. The accuracy rate is improved using wavelets along with the statistical differentiation of the various power signal disturbances. Index Terms-Discrete Wavelet Transform (DWT), Power Quality Disturbances, probabilistic neural network (PNN) feed forward Back propagation neural network(FFBPN), Multi Resolution Analysis (MRA).
Automatic detection and classification of Power Quality (PQ) disturbances plays a vital role for the protection of power system. In this paper, a Discrete Wavelet Transform based Probabilistic Neural Network (DWT-PNN) approach has been proposed for the automatic detection and classification. The DWT is used for the detection of the PQ disturbances. The statistical features such as energy values of the detail and approximation coefficients are obtained using multi-resolution analysis of DWT. Then the statistical features are used as the training data to the PNN classifier. The results demonstrate the proposed DWT-PNN classifier effectively detects and classifies the PQ disturbances with high accuracy.