Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances (original) (raw)
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Investigations on Power Quality Disturbances Using Discrete Wavelet Transform
Extensive use of power electronic devices and non-linear loads in electrical power system cause problem of power quality (PQ). Renewable energy sources are also integrated to the grid through power electronics based equipment. So the power quality issues are drawing attention in recent years. PQ disturbance need to be detected accurately It is also essential to find out the cause of such an event. Detection and classification of PQ disturbance helps to control such event. In this paper, wavelet transform is applied to notice, localize, and extract power signal disturbance. To study various power quality disturbances, a model simulated using MATLAB/Simulink toolbox. The key plan underlying in the approach is to decompose a given disturbance signal into alternative signals that represent a smoothened version and a close version of the first signal. Using multi resolution analysis signal is decomposed.
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.
Application of wavelet transform for power quality (PQ) disturbance analysis
This paper presents a wavelet analysis based time-frequency technique to identify power system transients and disturbances. A mother wavelet basis function (Littlewood-Paley (L-P)) with dyadic non-overlapping frequency hands has been used. The advantage of the proposed approach is in identification of the occurrence and duration of the disturbances. A few power signals with power quality disturbances have been analysed to show the effectiveness of the proposed technique.
Detection of Power Quality Disturbances Using Wavelet Transforms
A new method for detection of power quality disturbance is proposed: first, the original signals are de-noised by the wavelet transform; second, the beginning and ending time of the disturbance can be detected in time, third, determining the cause of power quality disturbances using various approaches such as Multi Resolution Analysis (MRA) or Discrete Wavelet Transforms (DWT) In this paper, wavelet transform is proposed to identify the power quality disturbance at its instance of occurrence. Power quality disturbances like sag, swell, interruption, DC offset, frequency variation and harmonics are considered and are decomposed up to 4 levels using Db4 wavelet. For some disturbances it is sufficient to have only second or third level of decomposition. The exact location of the disturbance can also be found on the time scale. The application to a case study shows that this method is fast, sensitive, and practical for detection and identification of power quality disturbance.
Application of wavelet transform to power quality (PQ) disturbance analysis
2004
This paper presents a wavelet analysis based time-frequency technique to identify power system transients and disturbances. A mother wavelet basis function (Littlewood-Paley (L-P)) with dyadic non-overlapping frequency hands has been used. The advantage of the proposed approach is in identification of the occurrence and duration of the disturbances. A few power signals with power quality disturbances have been analysed to show the effectiveness of the proposed technique.
Real-Time Detection and Classification of Power Quality Problems Based on Wavelet Transform
A new technique for real-time power quality (PQ) disturbances detection and classification based on wavelet multi-resolution analysis (MRA) is presented in this paper. The detection of start time, end time and duration of PQ event is based on the finest detail level of MRA while the classification of the event is based on the coarsest approximation level of MRA. LabVIEW platform has been used to implement the proposed technique in a laboratory setup. Several voltage events: interruption, swell and sag have been generated to test the performance of the proposed technique. The experimental results demonstrate the superiority, accuracy, and robustness of the proposed method in detecting the details of the voltage events as well as the event type classification. The effectiveness, accuracy and robustness of the proposed technique in the detection and classification of the PQ events have been demonstrated by experimental results. Moreover, the proposed technique shows a significant reduction in execution time with less complexity compared to conventional methods, for that the proposed technique is more suitable for online detection and classification applications.
Modern spectral and harmonic analysis is based on Fourier based transforms. However, these techniques are less efficient in tracking the signal dynamics for transient disturbances. Consequently, The wavelet transform has been introduced as an adaptable technique for non-stationary signal analysis. Although the application of wavelets in the area of power engineering is still relatively new, it is evolving very rapidly. The application of the wavelet transform in detection, time localization, and classification of power quality disturbances is investigated and a new identification procedure is presented. Different power quality disturbances will be classified by a unique energy distribution pattern based on the difference of the discrete wavelet coefficients of the analyzed signal and a pure sine wave. Verification of the proposed algorithm was done by simulating different disturbances and analyzing the results.
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.
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.
Ibrahim Detection of Power Quality Disturbances using Wavelet Transform
2009
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. Keywords—Power quality, detection of disturbance, wavelet transform, multiresolution signal decomposition.