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Construction of Finite Impulse Wavelet Filter for Partial Discharge Localisation Inside a Transformer Winding

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Construction of Finite Impulse Wavelet Filter for Partial Discharge Localisation Inside a Transformer Winding 2013 Electrical Insulation Conference, Ottowa, Onterio, Canada, 2 to 5 June 2013 Construction of Finite Impulse Wavelet Filter for Partial Discharge Localisation inside a Transformer Winding M. S. Abd Rahman1, P. Rapisarda2 and P. L. Lewin1 1The Tony Davies High Voltage Laboratory, University of Southampton, SO17 1BJ, UK 2Communications, Signal Processing and Control, University of Southampton, SO17 1BJ, UK Email: [email protected] Abstract- In high voltage (H.V.) plant, ageing processes can strategies [1]. Generally, timed-based preventative occur in the insulation system which are totally unavoidable and maintenance is performed periodically regardless of asset ultimately limit the operational life of the plant. Ultimately, condition which leads to higher operational costs. In order to partial discharge (PD) activity can start to occur at particular save maintenance costs, general CM practice is moving from a points within the insulation system. Operational over stressing and defects introduced during manufacture may also cause PD time-based approach to on-line based on specially condition activity and the presence of this activity if it remains untreated assessment [2,3]. Therefore, partial discharge condition will lead to the development of accelerated degradation processes monitoring for transformers and also PD source location along until eventually there may be catastrophic failure. Therefore, a transformer winding have become important research areas partial discharge condition monitoring of valuable HV plant such that aim to provide asset health information, enabling as a transformers and in particular along a transformer winding maintenance and replacement processes to be carried out is an important research area as this may ultimately provide effectively. To date, various methods of PD detection have asset health information enabling the maintenance and been proposed and discussed in the literature [3]. The proper replacement processes to be carried out effectively. Wavelet and suitable detection of real PD is an important element that multi-resolution analysis consists of a series of quadrature filter banks which are associated with a high pass and low pass filter. needs to be associated with PD measurement systems. The The process is performed in order to decompose original signals detection system needs to ensure that detection can be carried into different levels that contain different time-frequency out with high sensitivity, good noise rejection and is able to resolutions of the original waveform. Thus, the spread of signal differentiate between internal and external discharge events energy over different time/frequency ranges can be determined. [2]. There are several different sensing technologies that can The use of system identification in the frequency domain using be applied such as ultra-high frequency (UHF), acoustic and the Wavelet transform provides unique selections of the optical measurements which have been extensively particular frequency range of interest of the measured PD signals investigated [4]. Based on the assumption that the PD that have propagated inside a transformer winding. Wavelet measurement technique uses current measurement to detect decomposition levels can be combined linearly with Principal Component Analysis (PCA) and this may provide useful any PD signals flowing to earth via the bushing tap and neutral information about the location of the discharge source within the to earth connections, radio frequency current transducers winding and with further implementation using an infinite (RFCT) have been found to be an effective sensing method impulse response (IIR) filter approximation, it is possible to and have been proven to be sensitive enough for PD construct a standard filter based on the Wavelet transform and measurement of discharge currents over a frequency range of PCA that can be implemented as an automatic PD localization 10 kHz to 200 MHz. More recent research has analysed tool. information using data mining methods [5,6]. The benefit of signal processing techniques such as the Wavelet transform Keywords; partial discharge; transformer; condition monitoring; (WT) as a signal decomposition tool over different the Wavelet transform; system identification; PD location. frequency and time domain and data mining technique such as Principal Component Analysis (PCA) used as dimensional I. INTRODUCTION reduction tool is that when combined they can represent the distribution of energy of the captured PD pulse as a single In high voltage plant, ageing processes can occur in the point in three dimensional space. This property then may be insulation system which are totally unavoidable and ultimately used as an indicator of PD location inside a transformer limit the operational life of the plant. These processes can winding. In developing this analysis, we report on the increase the likelihood of unwanted partial discharge (PD) construction of standard finite impulse response filter (FIR) to activity inside a transformer and this activity will lead to directly perform this combined operation. The technique further ageing and degradation ultimately lead to a presented in this paper is mainly based on the linear catastrophic failure. To avoid this, regular condition combination of the wavelet decomposition filter which monitoring (CM) is generally implemented and there are consists of low pass, high pass and band pass filter for every traditional corrective and time based preventative maintenance level and the related PCA loading coefficients. Hence, 978-978-1-4673-4744-0/13/$31.00 ©2013 IEEE 30 transfer functions for the corresponding constructed filters are into two components, cA1 and cD1 by scaling function and estimated using the Yule-walk equation for an IIR filter wavelet function respectively. The cA1 is known as approximation. The theory of data mining techniques is approximation coefficient of level one is then again discussed in the next section and the experiments undertaken decomposed into a level consists of a new approximation, cA2 within the Tony Davies High Voltage Laboratory are and new detail coefficient cD2, the whole process of wavelet described in following section. Obtained results using the decomposition is shown in Fig.2. technique are presented and discussed as well as conclusions resulting from this work. II. FUNDAMENTAL METHODS A. Discrete Wavelet Transform The wavelet transform is a useful mathematical tool for time- frequency domain analysis. It has been applied effectively of partial discharge analysis in high voltage plant [6]. The basis of the wavelet transform requires selection of a proper mother wavelet (ψ(m)) according to the analysed signal. There are various type of mother wavelet, such as Symlet, Meyer, Coiflet, Morlet and Daubechies wavelet which have unique Fig. 2. Iterative wavelet decomposition process. properties and are suitable for different applications. In this paper, the Daubechies wavelet is applied due to the fact that TABLE I. WAVELET FILTERS AND THEIR MAIN INTERVALS [7]. this mother wavelet was found in the initial investigation is Decomposition Frequency Frequency Frequency domain filter able to ‘map’ the characteristics of the PD pulse and hence is level(n) (min) (max) believe to be effective in analysis of transient signal produced D1 by PD activity [1]. The wavelet plays a role of high-pass, 4 2 2 D2 band-pass and low-pass filter as shown in Fig.1. The high– · 8 4 4 2 pass filter and the low-pass filter corresponding to the wavelet D3 · · function and scaling function respectively. It is shown that the 16 8 8 4 2 approximation, A, of the original signal, S, sampled at fs = D4 · · · 500MSs-1 contains the smallest frequencies (in brown line) 32 16 16 8 4 2 Dn and the details contain highest frequencies (in red line). The · 2 2 2 2 set of whole wavelet filters for each decomposition levels is summarized in Table 1. An 0 2 2 B. Principal Component Analysis Principal component analysis is a non-parametric statistical method that is used widely and is the most popular dimensional reduction technique for large data sets and can reveal hidden patterns inside data [1,6]. Wavelet analysis generates a distribution of (n+1) energy variables over the frequency range. As such the individual PD pulse can be represented by a vector of these ten variables (or a single point in (n+1) dimensional spaces). In this paper, just three variables are used for visualization of the features hidden in the data sets, thus, PCA was used to extract these three values that best represent the (n+1) vector. Generally, the results of PCA analysis are used to project the actual values from the Fig. 1. Frequency domains of wavelet filters at different analysis levels. correlation matrix into different value of uncorrelated variables in principal component space, but the important The combination of the wavelet and scaling functions thing in order to determine the value of the scores is the produces a band-pass filter from decomposition level 2 principal component coefficients, also known as loadings (V). onwards. For computation, usually the discrete wavelet In order to determine these loadings, Eigen-analysis is transform is used, the process of decomposition involves an performed as follows: iterative process in which, the original signal is decomposed 31 1. The data is centered by zero mean and unity The method is applied in order to compute parameters of AR variances (standardization). model using
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