Wavelet Transform for Processing Power Quality Disturbances

Abstract

The emergence of power quality as a topical issue in power systems in the 1990s largely coincides with the huge advancements achieved in the computing technology and information theory. This unsurprisingly has spurred the development of more sophisticated instruments for measuring power quality disturbances and the use of new methods in processing and analyzing the measurements. Fourier theory was the core of many traditional techniques and it is still widely used today. However, it is increasingly being replaced by newer approaches notably wavelet transform and especially in the post-event processing of the time-varying phenomena. This paper reviews the use of wavelet transform approach in processing power quality data. The strengths, limitations, and challenges in employing the methods are discussed with consideration of the needs and expectations when analyzing power quality disturbances. Several examples are given and discussions are made on the various design issues and considerations, which would be useful to those contemplating adopting wavelet transform in power quality applications. A new approach of combining wavelet transform and rank correlation is introduced as an alternative method for identifying capacitor-switching transients.

Wavelet and PCA to Power Quality Disturbance Classification Applying a RBF Network

“…One area in which the DWT has been particularly successful is transient analysis in power systems (Santoso et al, 2000a(Santoso et al, , 2000bYilmaz et al, 2007) (Chen & Zhu, 2007). This capability to expand function or signal with different resolutions is termed as Multiresolution Analysis (MRA) (Mallat, 1989).…”

“…However, in this application, as in (Zhu et al, 2004;Chen & Zhu, 2007), the Daubechie "db4" wavelet was adopted to perform the DWT. As can be seen in (Daubechies, 1992), the "db4" is a wavelet of support four, i.e., each filter has four coefficients, 0123 hhhhh =   .…”

“…Further on, Chen and Zhu presented a review of the wavelet transform approach used in power quality processing. Moreover, a new approach to combine the wavelet transform and a rank correlation is introduced as an alternative method to identify capacitor-switching transients (Chen & Zhu, 2007). Taking into account these ideas, this chapter proposes the application of a different method of power quality disturbance classification by combining discrete wavelet transform (DWT), principal component analysis (PCA) and an artificial neural network in order to classify power quality disturbances.…”

Near Perfect Reconstruction Filter Banks for Power Quality Analysis

“…To classify the power quality events, feature vectors obtained from the waveforms are used. Different signal processing techniques are used for feature extraction in PQ events [4], speaking of which, the discrete wavelet transform (DWT) notes extensive use in the past years [5]. Main advantage of using DWT in power quality analysis is its ability to analyze the signal simultaneously in the time and frequency domains.…”

Contribución a la mejora de los sistemas de medida basados en el IEEE Std 1459-2000

“…La aplicación de la transformada wavelet en los sistemas de potencia incluye el análisis y la detección de transitorios electromagnéticos, calidad de la potencia y detección de huecos [76]- [77]. La ventaja de usar la transformada wavelet reside en la capacidad que tiene dicha transformada de proporcionar diferentes frecuencias de resolución manteniendo la información en el dominio del tiempo y la frecuencia, permitiendo de esta forma el análisis de las perturbaciones no estacionarias [78]- [79]- [80].…”