**Application of Wavelet Transform and Artificial Neural Network to Extract Power Quality Information from Voltage Oscillographic Signals in Electric Power Systems**

R. N. M. Machado1, U. H. Bezerra2, M. E. L Tostes2, S. C. F. Freire1 and L. A. Meneses1 *1Federal Institute of Technological Education, Belém, Pará 2Federal University of Pará, Belém, Pará* 

 *Brazil* 

## **1. Introduction**

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Post-operation contingencies analysis in electrical power systems is of fundamental importance for the system secure operation, and also to maintain the quality of the electrical energy supplied to consumers. The electrical utilities use equipments as Digital Disturbance Registers (DDR), and Intelligent Electronics Devices (IED) for faults monitoring, and diagnosis about the electrical power systems operation and protection. In general, the DDR and IED are intended to monitor the protection system performance and detect failures in equipments and transmission lines, and also generate analog and digital oscillographic registers that better characterize the disturbing events.

The oscillographic signals often analyzed in the post-operation centers are those generated by events that typically cause the opening of transmission lines due to the action of protective relays. So, these records are analyzed in detail to determine the causes and consequences of these occurrences within the electrical system. Although the software used in the post-operation centers presents numerous features for the evaluation of the recorded signals, the selection of the signals to be analyzed is done in a manual way, which leads to an analysis in an individual basis, and many of the oscillographic records that could help analyzing the occurrences are not evaluated due to the long time that would be spent to select them manually.

Another aspect to be noted is that the oscillographic records remain stored in the post-operation centers for time periods ranging from months to years. These records contain signals acquired in different parts of the electrical system, and the vast majority of them are no longer being considered in the analysis. These data, however, may contain important information about the behavior and performance of the electrical system that may precisely characterize the power quality problem due to a failure or disturbance.

Application of Wavelet Transform and Artificial Neural Network to

that have become the cornerstone of wavelet applications today.

**2.1 Wavelet analysis** 

Daubechies wavelet, db8.

Fig. 1. The Daubechies wavelet, db8.

**2.2 Discrete wavelet transform** 

wavelet.

Extract Power Quality Information from Voltage Oscillographic Signals in Electric Power Systems 179

The mathematics main branch leading to wavelet analysis began with Joseph Fourier (1807) with his frequency analysis theory, known as Fourier analysis. The first wavelet mention appears in the appendix of A. Haar's thesis (1909). Paul Levy a 1930's physicist, investigating the Brownian motion, found that the Haar basis functions are superior to the Fourier basis functions for studying small and complicated details in the Brownian motion. In 1980, Grossman and Morlet, broadly defined wavelets in the context of quantum physics, providing a way of thinking about wavelets based on physical intuition. In 1985, Stephane Mallat gave wavelets an additional advance. Through his work in digital signal processing, he discovered some relationships among quadrature mirror filters - QMF, pyramidal algorithm, and orthogonal wavelet basis. Based partially on these results, Y. Meyer built the first non-trivial wavelets, which unlike the Haar wavelet, the Meyer wavelets are continuously differentiable, but do not have compact support. Years later, Ingrid Daubechies used Mallat's work to build a set of wavelets with orthogonal basis functions

The wavelet transform is a technique similar to the windowed Fourier transform with the difference that the window width is variable. The wavelet analysis allows the use of large time intervals when it is desired to get low frequency information and shorter time intervals when the interest is to obtain high frequency information. Unlike Fourier analysis that uses sines and cosines, wavelet analysis uses wavelets. Figure 1 shows as an example, the

Wavelets sets are employed to approximate signals, and each set consists of scaled versions (compressed or expanded) and translated (time shifted) from a single wavelet, called mother

In the discrete wavelet transform the term "discrete" applies only to the parameters in the transformed domain, that is, scales and translations, and not to the independent variable time, of the function being transformed. The discrete wavelet transform provides a set of coefficients corresponding to points on a grid or two-dimensional lattice of discrete points in the time-scale domain. This grid is indexed by two integers, the first, denoted by *m* ,

One of the main difficulties in using measurements, obtained by DDR, in the evaluation of power quality as compared with those obtained by power quality monitors, is that many of the signal processing stages are not performed automatically by the first. For the oscillographic records to be useful as power quality indicators, it is first necessary to obtain certain parameters to classify the recorded signals according to the event type that has occurred. Considering the case of short duration voltage variations (SDVV), the parameters of interest are the event amplitude and time duration. Obtaining these parameters enables the application of statistical tools as presented in (Bollen, 2000), for results analysis and visualization, which allows having information about the electrical system behavior at certain time intervals, for example, months or years.

Another difficulty, perhaps the most critical, is the large volume of data obtained from oscillographic monitoring. Many of these recorded signals are due to switching maneuvers, or due to spurious signals or noise, without characterizing voltage changes in the electrical system. For this large amount of data to be evaluated, it is necessary that an automatic classification method be used so that only signals with the desired characteristics are used to determine the parameters of interest. This aspect is highlighted in several publications which present new methods for classification and characterization using digital signal processing and computational intelligence tools (Angrisani et al, 1998; Santoso et al, 2000a; Santoso et al, 2000b and Huang et al, 2002; Machado et al, 2009; Rodriguez et al, 2010 ).

The first use of wavelet transform in power systems is credited to (Ribeiro, 1994). In recent years, wavelet transform - WT, a powerful tool for digital signal processing, has been proposed as a new technique for monitoring and analysis of different disturbances types in power systems (Machado et al, 2009; Mokryani, 2010; A. Rodriguez et al, 2010; Gong Jing, 2010, 2011). Wavelets, along with computational intelligence techniques like artificial neural networks and fuzzy logic, have been used successfully in automatic classification of power quality problems. (Machado et al, 2009; Mokryani, 2010; Rodriguez et al, 2010)

The present work aims to develop an automated system for classifying power quality problems with respect to the fault type that has occurred and the electric phase involved, and quantify SDVV in electrical power systems from the available oscillography in the electrical utilities post-operation centers, to form a parameter database characterizing power quality problems. The proposed methodology uses the wavelet transform to obtain a characteristic pattern to represent the phenomenon and a probabilistic neural network for classification.
