Detection, localization, and classification of power quality disturbances using discrete wavelet transform technique (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 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.
Selecting wavelet functions for detection of power quality disturbances
2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, 2008
This paper considers Discrete Wavelet Transform (DWT) for the detection of power quality disturbances. Wavelet Function Biorthogonal 3.9 is used as a base function due to its frequency response and information time localization properties. A methodology is proposed in order to choose the wavelet function that holds the best characteristics. Disturbances considered are low frequency disturbances such as flicker and harmonics and high frequency disturbances such as transient and voltage sags. Due to time-frequency localization properties, Discrete Wavelet Transform permits decomposition of signals in different energy levels. The first level of decomposition allows for detecting the start or the end of a disturbance by convolving the high pass decomposition filter (HPDF) with the disturbance.
DIAGNOSIS 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.
2016
With the increase of non linear load such as a range of electronic and microprocessor base equipment power quality becomes the prominent issue now a day. In order to improve the power quality problem, detection of power quality problem must be done first. This paper presents a literature review of the application of wavelet transforms in the detection and analysis of power quality disturbances. The PQ disturbances include wide range of PQ phenomena namely transient (impulsive and oscillatory), short duration variations (interruption, sag and swell), power frequency variations, long duration variations (sustained under voltages and sustained over voltages) and steady state variations (harmonics, notch, flicker etc.) with time scale ranges from tens of nanoseconds to steady sate in this condition extraction become difficult task. This paper presents a comprehensive review of different techniques based on wavelet transform to detect and classify power quality problems and advantages of...
POWER QUALITY ANALYSIS VIA WAVELET TRANSFORM
The dependence of modern life upon the continuous supply of electrical energy makes power quality of utmost importance in the power systems area. In this paper work, a new approach to detect, localize and investigate the feasibility of classifying various types of power quality disturbances is presented, wavelet transform analysis is done as well as the concept of mother wavelet is also explained. In quality of power, the current state of art is the use of Daubechies wavelets. Daubechies wavelets belong to a special class of mother wavelet and actually they are the most used for detection, localization and classification of disturbances. The key idea underlying the approach is to decompose the disturbance signal developed with the help of matlab 7.0.5 version simulink into other signals which represent a approximated version and a detailed version of the original signal by using the wavemenu toolbox. The signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The signal firstly separated and then analysed using different techniques step by step. The decomposition is performed using multi-resolution signal decomposition techniques. The demonstration is done with the distribution system to detect and localize disturbance with actual power line disturbances. In order to enhance the detection outcomes, utilization of wavelet transform coefficients of the analysed power line signals. The results of various other methods are compared and presented the best suitable method. The simulation results clearly demonstrate the superiority and effectiveness of the wavelet transform in both current and voltage signal noise reduction.
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.
2012
This paper presents a new three phase approach based on space vector discrete wavelet transform to detect and localize power quality disturbances (PQD). This approach provides high resolution time frequency representation used to detect and localize the disturbances. Supplementary information about detected disturbances (duration and frequency spectrum) extracted in order to characterize them. From the monitored three phase voltage signals a space vector is generated using Clarke Transformation. For normal system voltage the space vector is of constant magnitude signal of 1.5pu. If PQD occurs in any one or all phases of system, results in change of magnitude or frequency or both of the space vector. The space vector is decomposed using Discrete Wavelet Transform (DWT) and the magnitude of detail coefficients is used to detect and localize the PQ disturbances. The proposed technique monitors all three phase voltages simultaneously therefore can offer fast detection than existing sing...
International Journal of Industrial Electronics, Control and Optimization (IECO), 2021
This paper presents a novel and simple approach to detecting and classifying a wide range of power quality (PQ) events based on the discrete wavelet transform (DWT) and correlation coefficient. For this purpose, two new indices are proposed and the type of PQ event is detected by comparing the values of the correlation coefficient between the value of these indices for the pre-stored PQ events and for a recorded indistinct signal. This algorithm enjoys the advantages of DWT and correlation coefficient and it does not suffer the disadvantages of neural networks or neural network-fuzzy based algorithms such as training and high dimension input matrices or the disadvantages of Fourier transform-based approaches such as unsuitability for non-stationary signals as it does not track signal dynamics properly due to the limitation of fixed window width. The effectiveness of the method tested by numerous PQ disturbance and simulation results confirms the competency and the ability of the proposed method in detection and automatic diagnosis of PQ disturbances. Compared with the other methods, the simulation under different noise conditions verifies the effectiveness of the noise immunity and the relatively better accuracy of the proposed method.