Time--frequency feature representation using energy concentration: An overview of recent advances

In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Wavelet-based neural network for power disturbance recognition and classification

This paper deals with the use of a continuous wavelet transform to detect and analyze voltage sags and transients. A recursive algorithm is used and improved to compute the time-frequency plane of these electrical disturbances. Characteristics of investigated signals are measured on a time-frequency plane. A comparison between measured characteristics and benchmark values detects the presence of disturbances in analyzed signals and characterizes the type of disturbances. Duration and magnitude of voltage sags are measured, transients are located in the width of the signal. Furthermore, meaningful time and frequency components of transients are measured. The whole method is implemented and tested over a sample representing recorded disturbances. Detection and measurement results are compared using classical methods.

Detection and measurement of power quality disturbances using wavelet transform

Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances.

Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks

This paper deals with the comparison of new signal processing tools for power quality analysis. Three new signal processing techniques are considered: the continuous wavelet transform, the multiresolution analysis and the quadratic transform. Their theoretical behaviours are investigated using the basic theory of the Fourier transform. Then, examples of the four most frequent disturbances met in the power system are chosen. Finally, each kind of electrical disturbance is analyzed with example representing each tool. A qualitative comparison of results shows the advantages and drawbacks of each new signal processing technique applied to voltage disturbance analysis. The continuous wavelet transform appears to be a reliable method for detecting and measuring voltage sags, flicker and transients in power quality analysis.

New signal processing tools applied to power quality analysis

This paper deals with the use of a continuous wavelet transform to detect and analyze voltage sags and transients. A recursive algorithm is used and improved to compute the time frequency plane of these electrical disturbances. Characteristics of investigated signals are measured on a time-frequency plane. A comparison between measured characteristics and benchmark values detects the presence of disturbances in analyzed signals and characterizes the type of disturbances. Duration and magnitude of voltage sags are measured, transients are located in the width of the signal. Furthermore, meaningful time and frequency components of transients are measured. The whole method is implemented and tested over a sample representing recorded disturbances. Detection and measurement results are compared using classical methods.

P. Rioual

Papers

Detection and measurement of power quality disturbances using wavelet transform

New signal processing tools applied to power quality analysis

Detection and measurement of power quality disturbances using wavelet transform