Profoundly Robust Power Quality Event Classifier Based on Wavelet Transformation and Artificial Intelligence (original) (raw)
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2015 50th International Universities Power Engineering Conference (UPEC), 2015
In this paper, detection method and classification technique of power quality disturbances is presented. Due to the increase of nonlinear load recently, it becomes an essential requirement to insure high level of power supply and efficient commotional consuming. Wavelet Transform represents a powerful mathematical platform which is needed especially at non-stationary situations. Disturbances are fed into wavelets to filter, detect and extract its features at different frequencies. Training of features extracted by WT is done using artificial neural networks ANN to classify power quality disturbances.
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.
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.
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.
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.
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.
Detection & Analysis of Power Quality Disturbances using Wavelet Transforms and SVM
This paper presents a new method for automatic classification of power quality events, which is based on the wavelet, transform and support vector machine. The proposed method for PQ event classification is divided into the three stages: pre-processing, feature extraction, and classification stage. In the proposed approach, we are using wavelet multi resolution analysis (WMR) to extract different features from the disturbance signal, an effective single feature vector representing three phase event signals is extracted after signals are applied normalization and segmentation process.ATP/EMTP model for six types of power system events, namely phase-to-ground fault, phase-to-phase fault, three-phase fault, load switching, capacitor switching and transformer energizing, are constructed. Both the noisy and noiseless event signals are applied to the proposed method using test model (WECC9 bus system). Obtained results indicate that the proposed automatic event classification algorithm is robust and has ability to distinguish different power quality event classes easily.
Electric Power Systems Research, 2008
This paper presents a wavelet norm entropy-based effective feature extraction method for power quality (PQ) disturbance classification problem. The disturbance classification schema is performed with waveletneural network (WNN). It performs a feature extraction and a classification algorithm composed of a wavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The PQ signals used in this study are seven types. The performance of this classifier is evaluated by using total 2800 PQ disturbance signals which are generated the based model. The classification performance of different wavelet family for the proposed algorithm is tested. Sensitivity of WNN under different noise conditions which are different levels of noises with the signal to noise ratio is investigated. The rate of average correct classification is about 92.5% for the different PQ disturbance signals under noise conditions.
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.
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.