Review of Signal Processing Techniques for Detection of Power Quality Events (original) (raw)
Related papers
2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2013
The challenging process industry requires true power for its smooth functioning and here comes the importance of good power or power. The term Power Quality (PQ) aims at supplying true power to the process. The scope of the power quality increased with the introduction of newly designed sophisticated devices like computers and microcontrollers. The performances of these devices are extremely sensitive to the various power quality problems. The mainly occurring PQ problems are voltage sag, voltage swell, voltage flickers, harmonics distortions etc. The concept of power quality became increasingly complex and vital with the introduction of recently designed sophisticated and sensitive devices, whose real time performance is extremely subjective to sensitiveness of the supply. Power Quality (PQ) has turned to be a serious issue to electricity consumers at all levels. Power quality is a major concern to electricity consumers today. The sensitivity factor of the power electronic equipment and non-linear loads to the input excitations voltages are widely used in process control as well as individual consumers which lead to the PQ problem. The paper gives a brief review in accordance with relevant literature surveys classifies the various electric power quality disturbances using wavelet transform analysis. The survey includes detection voltage disturbances and categorization of the type of event. The power quality analyzer is designed and used to measure the occurrence and classification of PQ events. Malfunction of the equipment will happens when the power failure occurs. Several signal processing techniques for the detection and classification of these disturbances are studied and discussed here. The detection techniques are mainly based on signal averaging, RMS method, Kalman Filter method, Fourier Transforms, Wavelet Transforms etc. Wavelets and fast Fourier transforms are of major importance in the classification.
Detection and Classification of Multiple Power-Quality Disturbances With Wavelet Multiclass SVM
IEEE Transactions on Power Delivery, 2000
This paper presents an integrated model for recognizing power-quality disturbances (PQD) using a novel wavelet multiclass support vector machine (WMSVM). The so-called support vector machine (SVM) is an effective classification tool. It is deemed to process binary classification problems. This paper combined linear SVM and the disturbances-versus-normal approach to form the multiclass SVM which is capable of processing multiple classification problems. Various disturbance events were tested for WMSVM and the wavelet-based multilayer-perceptron neural network was used for comparison. A simplified network architecture and shortened processing time can be seen for WMSVM. Index Terms-Disturbances-versus-normal (DVN) approach, power-quality disturbances (PQD), support vector machine (SVM), wavelet multiclass support vector machine (WMSVM). I. INTRODUCTION T HE POWER-QUALITY (PQ) study has become a more important subject lately. Harmonics, voltage swell, voltage sag, and the power interruption could downgrade the service quality. In recent years, the high-speed railway (HSR) and massive rapid transit (MRT) system have been rapidly developed, with the applications of widespread semiconductor technologies in the autotraction system. The harmonic distortion level worsens due to the increased use of electronic equipment and nonlinear loads. To ensure the PQ, power disturbances detection becomes important as well to further detect the location and disturbance types. Traditionally, PQ was judged by visual inspection of the disturbance waveforms, so the engineer's knowledge plays a critical role. As always, the PQ engineer is inundated with an enormous amount of data for inspection. It is desirable to develop automatic methods for detecting, identifying, and analyzing various disturbances [1]-[4]. Fast Fourier transformation (FFT) [2] has been applied to the steady-state phenomenon but short-time duration disturbances require the Manuscript
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.
Power Quality Identification Using Fast Fourier Transform and Wavelets
MediaTEK, 2011
The objective of electricity utility is to deliver sinusoidal voltage at fairly constant magnitude and frequency throughout their system. However, most of intensive users of electricity are suffering to a certain poor quality of electrical power, while others have been adopted the solution to deal with. Therefore, detection are essential to describe such event. There are various methods to identifying power quality problems. The method to detect power quality discuss in this paper are Fast Fourier Transform or FFT and Wavelets. The FFT plays important roles in analysis, design and implementation of disctere signal processing. FFT algorithms are based on fundamental of discrete fourier computation. Such algorithm are more efficient than the discrete fourier transform. On the other hand, Wavelets is mathematical functions that cut up data or signal into different frequency components and study each component with a resolution matched to its scale. This paper highlights the FFT and Wavelets to identify power quality problems even present with noise in Matlab environment. The result of simulation shows that Fast Fourier Transform and Wavelets work accurately and nicely. Keywords: Fast Fourier Transform; Wavelets; Signal Processing.
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).
… Transactions on Power …, 2006
This article presents a classification method regarding voltage disturbance for three-phase signals obtained from disturbances recorded data in electric power systems. The proposed method uses wavelet transform to obtain a characteristic vector for voltage phases a, b and c, and a probabilistic neural network is used for classification. The classified signals as presenting voltage disturbance will form a database, being then available for future analyses. The results obtained with the application of this proposed methodology to a real system are also presented.
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.
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.