Autonomous Localization of Partial Discharge Sources ... - IEEE Xplore

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M. S. Abd Rahman et al: Autonomous Localization of Partial Discharge Sources within Large Transformer Windings

Autonomous Localization of Partial Discharge Sources within Large Transformer Windings M. S. Abd Rahman Universiti Tenaga Nasional (UNITEN) Jalan Ikram-UNITEN 43000 Kajang Selangor Darul Ehsan, Malaysia

P. L. Lewin and P. Rapisarda The Tony Davies High Voltage Laboratory School of Electronics and Computer Science University of Southampton Southampton, SO17 1BJ, UK ABSTRACT Partial discharge (PD) condition monitoring inside a HV transformer generally and particularly along a transformer winding has become an important research area with the ultimate aim of providing asset health information that enables maintenance and replacement processes to be carried out effectively. As far as PD activity inside transformer windings is concerned, an electrical detection method has been developed based on the use of radio frequency current transducers and subsequent digital signal processing of captured measurement data. A localization approach based on the measurement of currents at the bushing tap point and neutral to earth connection has been developed, with the assumption that different PD source locations will generate unique signal profiles in terms of the distribution of measured current energies with respect to both frequency and time. Therefore the technique presented is based on analysis of measured current energies associated with different frequencies. Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the data, whilst minimizing lost information in the original dataset. This non-linear analysis of captured current data is not practicable for the field but the process can be represented through the use of three finite impulse response filters that have the ability to perform PD source localization automatically and are straightforward to implement in monitoring hardware. Index Terms - Partial discharges, wavelet transform, signal processing, power transformer insulation system.

1 INTRODUCTION MAINTENANCE approaches of high voltage transformers in the field have been developed over recent years. There are several traditional approaches employed for transformers, either corrective or time based preventive maintenance strategies have been generally implemented [1]. The corrective strategy involves implementation of maintenance once the power station or substation has failed to operate. The advantage of this type of maintenance is that it does not require any decision tools. However, using this approach will cost the operator a significant amount to replace the asset once it has failed. Meanwhile, time-based preventive maintenance can be implemented and has advantages over corrective strategies due to the fact that assessment of Manuscript received on 18 November 2014, in final form 2 October 2015, accepted 18 November 2015.

transformer health is performed periodically regardless of asset condition. However, there are disadvantages associated with this approach: high costs may be incurred due to unnecessary maintenance and, failure may still occur if maintenance is not well scheduled. To date there is no established method to predict the future condition of high voltage plant in operation and some degradation indicators, such as partial discharge activity within a winding, are difficult to detect as they depend on a large number of internal and external factors. Partial discharge (PD) as defined by IEC 60270 [2], if unequivocally recognized as generated by an internal source, is a clear indication of insulation degradation within transformers. Detection and measurement of PD activities for maintenance purposes needs additional decision tools which are required to determine the most appropriate maintenance processes. With the development of such tools, traditional transformer monitoring techniques will be

DOI: 10.1109/TDEI.2015.005070

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gradually replaced by online condition monitoring systems. On-line condition monitoring techniques offer increased accuracy, continuous cost reduction and ultimately diagnostic improvement due to sensor development and development of appropriate solutions in terms of data acquisition, information technology and intelligent diagnostic software [3, 4]. A detailed survey on high voltage transformer condition monitoring can be found in [5]. Currently, there are a range of PD measurement techniques which can be subdivided with respect to the form of energy being detected e.g. thermal, chemical, acoustic, optical or electrical means and these approaches have been widely reported [6-8]. The positive identification of real PD is an important element that needs to be associated with PD measurement systems. The detection system needs to have high sensitivity, appropriate noise reduction and the ability to differentiate between internal and external discharge events [6, 9]. Particular sensing technologies that can be applied to PD detection within transformers include conventional electric (apparent charge based), ultrahigh frequency (UHF), acoustic and optical approaches [9]. However, for this research, the PD measurement technique chosen uses radio frequency current transducers (RFCT) for measurement of the PD-induced signals acquired at the winding terminals [6, 7]. The assumption is that any PD signals travelling along a winding will flow to earth via its bushing tap point and neutral to earth connection. Although, internal partial discharge in a transformer may occur anywhere within the tank; this paper is concerned about locating partial discharge sites within the transformer windings themselves. PD activity at such a site will cause propagation of the discharge signal along the winding that ultimately may be detected in the form of current pulses and oscillations at the bushing tap point and neutral to earth connections. Hence, the analysis here is based on the hypothesis that such measurement data contain information that allows location of PD sources along the transformer winding. Consequently, a method has been developed for autonomous PD localization based on analysis of the distribution of energy within detected PD signals and subsequent clustering within a 3D space. The developed analytical technique has then been approximated using three finite impulse response filters that are straightforward to implement in digital PD monitoring equipment. 1.1 MODEL WINDING FOR EXPERIMENTS In the experiments performed to create data for signal analysis a high voltage transformer winding model immersed in mineral oil (conforming to uninhibited BS148:1998 class 1 and 60 kV transformer bushing OIP type were used in order to simulate PD activity inside a transformer winding. The transformer winding was manufactured by Alstom and incorporates an interleaved disc and a plain disc winding. Each winding consists of 7 discs pairs (14 discs) stacked on each other. Each disc has 14 turns and a pair of discs is called a section. The windings are wrapped around cylindrical central core that is represented using an aluminium cylinder. Both windings have the same construction size and use copper conductor wrapped with paper insulation and the winding voltage class is 30 kV rms which meets the standard requirement of IEC27260. The PD signals were generated

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using common artificial PD sources that normally occur inside transformer winding. The signals generated were injected into the interleaved winding via external terminals whilst the plain disc winding remained grounded. The interleaved winding consists of eight terminals, terminal one of the winding was connected to the bushing core bar while the last terminal was connected to a common grounding system. The arrangement was connected to an ac transformer rated 300 kV (called HV supply in Figure 1.1) via copper conductors to apply ac voltage to the samples (PD sources). The supply and connections were checked beforehand to ensure that they were PD free. Based on the theory of travelling waves [10], the PD signals generated from a discharge site will travel in both directions towards both ends of the winding, thus, there are two external measurement points located at the bushing tap point which is connected to terminal 1 while another sensor is located at the neutral to earth connection which is connected at terminal 8. Assuming that partial discharge events will induce discharge current flowing towards earth via these two measurement points, the current was measured using radio frequency current transducers (RFCTs). The RFCT used for this test is the clamp-type split core RFCT EMCO model 93686-5, serial model 9802-50174 which has a measurable frequency range from 10kHz, to 200MHz. Figure 1.1 shows a schematic diagram of the experiment to inject signals into transformer winding and study propagation behavior of PD signals.

Figure 1.1 An experiment model to simulate partial discharge activity inside a transformer winding.

During PD experiments, the measurements were captured using a digital storage oscilloscope, Tektronix DPO7254 with a bandwidth of 2.5 GHz and sampling rate 40 GSs-1 that records the complete time signature of each signal pulse using a sequence-mode technique that was used to display, analyze and store the obtained output signals from both sensors. The outputs of the acquisition process consist of a sequence of pulse signals (the input data for the Wavelet analysis) along with their associated time pointers to form a pulse train having a large number of pulses for each cycle of applied voltage. The entire instrumentation has a bandwidth of up to 200 MHz and operates at a suitable sampling rate which in this case is

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500 MSs-1 to avoid frequency aliasing. The measurement system is fully remotely-controllable by a personal computer. A coupling capacitor was adopted in the tests using an indirect connection. PD detection was performed using the MPD 600 (PD detector) and input unit which were both connected to a personal computer via fiber optic cable for detecting PD activities during the test and subsequent analysis (e.g PRPD plots for each experiment). Prior to any measurement, the noise level was determined and in terms of the RFCT outputs an acceptable background noise level was found to be around 6 mV and measured using PD detection around 600 fC as shown in Figure 1.2 and Figure 1.3 respectively.

Figure 1.2 A noise level measured during PD tests were performed.

Figure 1.3 PD detection sensitivity in “pC”.

Thus, the low background noise will not affect sensitivity of the equipment during PD measurement. The initial tests were performed and the data was first measured at 25-30 percent higher than PD inception voltage (PDIV) levels. However, the inception and extinction voltages may vary for different PD sources and also different test conditions (i.e. test sample or arrangement). A range of tests were completed at various applied voltages in order to study the influence of voltage levels on PD source localization, it was found that for the methodology proposed, the value of voltage level above the inception voltage had no effect on algorithm performance.

2 PD SIGNAL ANALYSIS In this investigation, Wavelet Transforms and dimension reduction using principal component analysis (PCA) have been applied to approximately four hundred PD signals recorded over 50 cycles of a.c. applied voltage. However, prior to analysis, the PD data were pre-processed in order to identify the matching pair of signals from the two different sensors as well as de-noising the raw data using a hardthresholding method. As a result, the PD signals from each experiment were extracted and stored as an individual data set. 2.1 WAVELET TRANSFORM Wavelet Transforms (WT) have previously been applied in this field due their ability to provide information about PD pulses in both the time and frequency domain within specific frequency ranges [11,12]. The initial stage of the algorithm involves applying Wavelet decomposition to individual PD pulses. The Wavelet decomposition process acts as a pair of complementary high-pass and low-pass filters which decompose the original signal (S) into a series of detail (D) and approximation (A) coefficients respectively, as shown in Figure 2.1a. The operation of the transform is an iterative process where the approximation coefficients are used as the new input for the next decomposition level, as symbolized in Figure 2.1b. Within the schematic, An and Dn represent the approximate and detail decomposition coefficients respectively and n denotes the decomposition level. The original PD pulse can be decomposed into a user-defined number of levels which need to be designed in order to avoid zero spanning inside the coefficients. Each level is half the bandwidth (sampling rate in frequency domain) and half the length (sample number in time domain) than the level from which it was derived, the operation of this analysis tool effectively characterizes the original signal at various frequency ranges that are then represented by coefficients at each node of the decomposition tree. After the filtering process is complete, the signal is represented as a series of scaled and shifted transforms of a user-defined basis function: the Mother Wavelet (ψ). One of the advantages of WT over traditional signal processing techniques is that the Mother Wavelet can be selected having taken into account the characteristics of the signal under analysis. An initial investigation discovered that the choice of the "Daubechies" family of order 9 as the Mother Wavelet produced a highly effective decomposition of PD-like signals. However other Wavelet families have also been considered and shown to also produce effective decomposition [13-15]. In this work, each individual PD pulse was decomposed into 9 levels due to the size of the input pulse length and also limited by Wavelet filter length i.e. the Mother Wavelet and its order. An example of the decomposition of PD signals recorded using an oscilloscope with a sampling rate of 500 MSs-1 and 2500 quantization levels, is shown in Figure 2.2 and the obtained decomposition coefficients were denoted as Detail D1 through to Detail D9 and Approximation A9 respectively. When selecting the appropriate decomposition levels during analysis,


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∑

∙ 100 1

Similarly the signal energy of the final approximation level is defined as: ∑ ∙ 100 2 where n is the decomposition level, N is the number of coefficients, Cd is the detail decomposition coefficients and Ca is the approximate decomposition coefficients. Bs and Nr are both represent the total of pulse energy measured at bushing tap and neutral to earth connection respectively, given by:

3

a

b Figure 2.1. The concepts of the discrete wavelet transform. a) fundamental filter decomposition b) iterative decomposition process.

an appreciation of the bandwidth and frequency response of the measurement system is critical. Determining the decomposition levels of interest involves selecting the frequency ranges of greatest significance - given the characteristics of the signals of interest. For nine levels, the decomposition coefficients are in the range of 125-250 MHz (D1), 62.5-125 MHz (D2), 31.3-62.5 MHz (D3), 15.63-31.3 MHz (D4), 7.81-15.63 MHz (D5), 3.91-7.81 MHz (D6), 1.953.91 MHz (D7), 0.98-1.95 MHz (D8), 0.49-0.98 MHz (D9) and 0-0.49 MHz (A9) respectively. It is assumed that the selected decomposition coefficients can effectively represent the unique characteristics of PD pulses from a specific discharge source location. The specific frequency bands that are defined by each set of wavelet coefficients are dependent on the bandwidth and sampling rate of the testing equipment. However, due to the high dimensionality of the wavelet coefficients themselves (i.e. the number of data points in each detail level), it is not possible to use them directly to analyse PD signals to determine source location. Therefore, the distribution of absolute signal energy in each decomposition level is introduced and defined as:

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The use of energy levels were found to be more effective at representing the PD pulses than the wavelet decomposition coefficients themselves in terms of data dimensionality reduction and removal of the influence of pulse-polarity [16]. A histogram displaying the energy of the decomposition levels of the wavelet coefficients for two coupled pulses (in Figure 2.2a and 2.2b) are shown in Figure 2.3a and 2.3b. Analysis of the energy distribution histograms facilitates easy comparison between different pulses. Comparison of Figure 2.3a and Figure 2.3b shows the energy associated with each set of wavelet decomposition coefficients derived from each signal is different. Thus, the implementation of this data mining technique allows for comparison of PD pulses and can be used as a feature for localization of the source along a transformer winding. Furthermore, on the basis of the empirical observation that the majority of signal energy of the original pulse can be concentrated in less than five decomposition levels, a further data reduction for visual inspection of the clusters can be obtained by the utilization of PCA. 2.2 PRINCIPAL COMPONENT ANALYSIS PCA is a non-parametric statistical method that is extensively used for dimensionality reduction as well as simplifying the structure of complex data sets, by means of an orthogonal linear transformation in the direction of the greatest variance [14-18]. PCA is applied here to reduce the number of energy levels associated with each decomposition level to three values, thus allowing a representation of the PD signal energy in 3-D space while minimizing information lost in the data reduction. The sample PD data sets (M) considered are expressed as Nc vectors, each comprising of ten dimension columns (the energy levels). The PCA procedure is produced from solution of an eigenvalue problem and implemented as follows:

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The data is standardized by subtraction of the mean for each dimension to get a centered observation (zero mean and unity variance). Determination of the (empirical) covariance matrix, C, given:

-

∙ 1

5

Calculation of the eigenvalues (λ) and the eigenvectors (υ), Arranging the eigenvalues in descending order, The scores (S), projection of the input data to the new coordinates defined as:

∙

6

a a

b Figure 2.2. Coupled PD signals from 14 kV void discharge at terminal 6 and their associated wavelet decomposition coefficients a. Bushing tap point b. Neutral to earth connection.

b Figure 2.3. The distribution of PD signal energy histogram obtained after wavelet decomposition of the example original (from Figure 2.2) signal measured at a) bushing tap point b) neutral to earth connection point.


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Each row of the projection represents the principal components with a decreasing significance. Dimensionality reduction: selecting the three highest order of variance in the new dataset and discarding the remaining order principal components. Plot the new representation of the data in a 3-D space.

Applying this algorithm on the data set, using the distribution of energy within the levels of the wavelet transform as the input data, the calculated energy vectors can be further reduced and ultimately can be visually presented. Analysis of the data as represented in 3-D space reveals information regarding the source of the captured PD signals. In order to determine the minimum dimension of the data set that can be used for dimensionality reduction whilst retaining as much information as possible, analysis of singular values of principal component is vital [19]. Figure 2.4 shows a scree plot of the principal components of a PD measurement data measured from one measuring sensor. There are ten dimensions of the data and the number of principal components can be identified using the largest percentage of reduction from one principal component to the next principal component. Due to the fact that, the biggest reduction (approximately 86 %) occurs between the third and the fourth principal components, the first three principal components are believed to be adequate to represent the dataset. Therefore, the results presented here are based on analysis of three principal components.

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combination of the wavelet decomposition filter which consists of low pass, high pass and band pass filter for every level and the related PCA weighting. Hence, transfer functions for the corresponding constructed FIR filters are estimated using the Yule-walk equation for an IIR filter approximation. Ultimately, the finite impulse response filters (FIR) can directly perform the combined operation of PD analysis for PD localisation producing equivalent cluster plots in 3-D space as shown in Figure 3.1.

Figure 3.1. A schematic diagram of 3 dimensional filters for on-line application using wavelet and PCA.

The individual wavelet function is an important basis for filter design which plays a role of high-pass, band-pass and low-pass filter; where the high–pass filter and the low-pass filter correspond to the wavelet function and scaling function respectively. i.e. An

Dn

Figure 2.4. A singular values plot of principal component analysis.

3 FILTER DESIGN In order to facilitate the PD based on-line condition monitoring using this form of analysis, a practical method that can be used to automatically detect the location of PD within a transformer winding is required. Therefore, further work has developed an approach that is straightforward to implement. As the wavelet transform consists of a low pass and a high pass filter in the wavelet filter bank and this can be implemented in the form of either finite impulse response or infinite impulse response filters. In particular the Daubechies wavelet filters are finite impulse response with 2p coefficients where N = 2p-1 is the number of wavelet order [20]. The proposed implementation is therefore based on the linear

2

2

∙

, 7

2

8

, are the frequency ranges of where 0, 1 and 2 detail and approximation levels respectively. The set of whole wavelet function for each decomposition level is shown in Figure 3.2. Hence, Figure 3.3 shows that the approximation, A9, the scaling function contains the lowest frequencies and the details, D1, the wavelet function contains highest frequencies which are represented using dashed and dotted lines respectively. The combination of the wavelet and scaling functions produces band-pass filters representing D2-D9. The PCA weighting, α, β and γ of the three principal components has been derived from a measurement dataset obtained from a model transformer winding (described in Section 1.1). From the wavelet filters and PCA weighting, the frequency response of three dimensional filters can be obtained using a linear combination given by:

, 2

9 9

, 2

9 10

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, 2

9 11

where α, β and γ are the first, second and third principal component weighs. Each derived frequency response of the filters is unique and is representative of the principal components. Figure 3.4 shows that the normalized frequency response of each filter for a typical pulse are significantly different.

Figure 3.4. Frequency spectrum of filter A, B and C for typical measured data.

Figure 3.2. Wavelet ‘db9’ function for each decomposition levels.

Figure 3.5. A Pseudo-diagram of three dimensional principal component filtering process for PD localisation within a transformer winding.

Figure 3.3 Frequency domains of wavelet filters at different analysis levels from the approximation centered at the lowest frequency through to Detail 1 centered at the highest frequency.

The frequency responses computed from the wavelet filters in the frequency domain for each individual decomposition level are combined linearly with PC weighting and the total response for each filter corresponds to the overall process described in Section 2. Before the filtering process of PD data can be performed; transfer functions of the filters using the

spectra estimation method were obtained [21]. In order to obtain the corresponding filter coefficients according to the spectra, the auto-regressive moving average (ARMA) technique is used and it is based on spectral estimation [22]. The result of this approach is to obtain parameters for filters based on infinite impulse response (IIR) to approximate the finite impulse wavelet filter. The parameters of an autoregressive (AR) model has been calculated with the aid of modified yule-walk equations (MYW) while the moving average (MA) parameter has been computed directly from unbiased sample correlation coefficients, and the estimated AR parameters, [23]. All the calculation procedures were undertaken using signal processing software package. The optimum selection of filter order during implementation to produce a cost effective solution whilst maintaining the accuracy of the result has been previously discussed [24] and


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in summary, analysis indicated that the order of filter should not be less than 25 or more than 50. The input signals as PD data from both measurement sensors; namely, at the bushing tap point and neutral to earth connection pass through the filters and the energy of each filtered output calculated (Figure 3.5).

4 PD SOURCE LOCALISATION The WT algorithm has been applied to PD signals and the distribution of energy values expressed as feature vectors. Subsequently, they were compressed to their respective PCA values to reduce the dimensionality of the feature vectors. For any PD activity that occurs at a different point along a transformer winding, the energies of the signals associated with each decomposition level will be significantly different. This can be seen by plotting all of the results of the PD analysis in 3D space and clustering pulses having similar PCA values together. A. CLUSTERING TECHNIQUE In the clustering of PD data, the hypothesis is that different PD source locations would generate different distances of separation distance between the individual clusters obtained from the two measurement points at opposite ends of the winding. Figure 4.1 shows eight subplots of the clusters for all the injection terminals along the winding obtained from two different sensors. There are two different groups of clusters separated from each other; the blue cluster represents the bushing tap point while the red cluster represents the PD signals measured at the neutral to earth connection end. The initial study based on the validity of using clusters, which measured cluster coherence and separation; found that the optimal number of clusters that can be derived from the PD data is two. Thus results displayed always show a single cluster for data from each measurement point. The cluster plot in Figure 4.1 demonstrates the change in separation between both cluster groups from terminal one to terminal eight. All the maximum values of the clusters are unified as shown as at horizontal and vertical axes. However, due to the presence of outliers which represent unwanted noise thus one or two of the axes are not unity. The largest separation distance between two groups of clusters occurs when PD signals are injected into terminals one or eight, whereas the separation between the clusters reduces when the source is more centrally located. Moreover, the cluster associated with the sensor that is closest to the source has the largest positive first principal component. By inspection, the proposed method is effective at indicating the general location of a PD source along the winding, however, some of the clusters produced are scattered and it is assumed that the measured data may contain small magnitude events that consequently have lower signal to noise ratios (e.g Figure 4.1c). B. SEPARATION DISTANCE To determine the separation between two clusters, a cluster’s centroid C, within the three coordinate systems for three planes (xyz) is used:

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,

1

,

1

, 12

a

b

c

d

e

f

g

h

Figure 4.1. 3-D principal component plots representing the three principal components derived from the absolute energies of void discharge signals injected at a) Terminal 1 b) Terminal 2 c) Terminal 3 d) Terminal 4 e) Terminal 5 f) Terminal 6 g) Terminal 7 h) Terminal 8.

where S is the principal component values and N is the number of PD pulses. The separation distance (SD) between two clusters was calculated using Euclidean distance from both centroids and is defined as:

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The separation values do have positive and negative values according to the way the blue and red clusters are plotted. In this work, the blue clusters is taken as a reference where when the blue clusters are plotted in positive region the separation distance is taken as positive value. Plots of separation distances for four different PD sources injected into the winding terminals are shown in Figure 4.2. The separation for terminal 1 is approximately 0.5 which is the largest positive distance and the separation distance approaching zero towards mid-point of the winding near to terminal 5. On the other hand, the group of blue clusters will rotate from that point and

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keep increasing in distance from zero to -0.5 at terminal 8 which the largest distance (negative distance is an indicator that rotation of the blue cluster had occurred and the clusters in negative side of principal component region. This also indicate that the location of PD is beyond mid-point (terminal 5) of the transformer winding).

a

c

a

b

c

d

e

f

g

h

b

d

Figure 4.2. Separation distance for different PD sources a) Void discharge b) Surface discharge c) Corona discharge d) Floating discharge.

5 THREE DIMENSIONAL FILTERS The previous results have shown that the combination of wavelet analysis and dimensional reduction process using PCA are able to define two separate clusters in 3-D principal component space which represent the measured PD signals from the two sensors at both end of a winding. Based on the aim of this research to produce an autonomous PD location detection, three standard filters Fa, Fb and Fc have been constructed based on the wavelet transform coefficients and PCA. The frequency responses of the three filters have been shown to be distinct in the previous section and it is assumed that they will produce equivalent results to those obtained using the full analytical technique. This will have the benefit of extracting the pertinent features from the measurement data without the necessity of having to perform a wavelet transform and PCA. There were two sets of PD data which generated using real discharge source which have been used to construct the filters as well as input data. From the construction of standard finite impulse response filter based on Wavelet Transform, the results from the filter are displayed in terms of energy points in Figure 5.1. The energies Ea, Eb and Ec are represent the energies calculated from the filter outputs Fa, Fb and Fc respectively. The output from different filters will produce output signal which different energy levels depend on where the location of PD occurred along a transformer winding. In the other words, the energy for measurement at the bushing tap point will be larger than the neutral to earth connection when PD occur at terminal 1 and vice versa. Similarly, the blue and red clusters represent the bushing tap point and neutral to earth connection respectively.

Figure 5.1. 3-D plots representing the three energies obtained from the filter process a) Terminal 1 b) Terminal 2 c) Terminal 3 d) Terminal 4 e) Terminal 5 f) Terminal 6 g) Terminal 7 h) Terminal 8.

Figure 5.2. A separation distance for 3-dimensional filters.

The produced result obtained from the filters shown that for a PD source at terminal 1, the signals measured at the bushing


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tap point (blue clusters) have higher energy than those detected at the Neutral to Earth point (red clusters). Inspection of the results presented in Figure 5.1 shows that similar trends are observed as for the clusters shown in Figure 4.1 and Figure 5.2 shows a separation distance plot for all terminal windings with respect to the cluster plots in Figure 5.1. The separation distance is calculated using the Euclidean distance between the centroids and a similar result obtained to that shown in Figure 4.2.

6 DETERMINATION OF MAXIMUM SEPARATION It is proposed to use a pulse injection technique, not to determine the ‘sensitivity’ of the measurement or in an attempt to gauge the severity of any PD activity but to establish the maximum separation that can exist between the two clusters for a given winding. Figure 6.1 shows a cluster plot for pulses injected into the bushing core bar of the experiment which was selected as a reference point. It is assumed to have a greatest difference in terms of percentage of energy distribution for bushing tap and neutral to earth measurement data due to the electrical distance apart from both measurement points which represent the full length of the model transformer winding. Together with separation distance measured for each terminals and this separation of the reference point, location of PD source along a transformer winding can be determined. The separation value when the PD signals were injected at the bushing core bar is 0.68 which show that it is the greatest separation values can be obtained from all the cluster plots. The ratio of separation length with respect to the bushing core bar can be used to produce a standard value which can be indicate the location of PD along a transformer winding without the need for interpretation of cluster plots and this ratio can be obtained by:

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winding where the value near to 1 will indicate that the location of PD is near to the bushing tap point while if the ratio is near to zero means near to center of winding. On the hand, this ratio also useful if location of PD needs to be determined for different types of transformer winding having different sizes and dimensions.

7 CONCLUSIONS A method has been proposed and detailed for locating partial discharge activities inside a transformer winding. Primarily, the proposed method is based on the analysis of wavelet decomposition and Principal Component Analysis of pulse-like signals measured at accessible points of a particular winding. This has led to the development of an autonomous PD locating tool that uses a series of filters designed based on the mathematical operations of the full analysis. The performance of both approaches have been evaluated using a model winding under laboratory conditions and results indicate that the proposed approach is feasible and with further development may provide an autonomous tool capable of determining the location of a PD source within a transformer winding without the need for human intervention compared to the existing methods using different techniques such as transfer function based method. Further work will involve field measurements of real power transformers having more complex construction which suspected of suffering from PD based on prior analysis of DGA data.

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[7] [8] [9] [10] [11]

Figure 6.1. 3-D plots representing the three principal components derived from the wavelet coefficient energies of pulses at reference point.

, will give a Interpretation of this value and using the clear indication of the location of the PD source along a

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[12]

[13]

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M. S. Abd Rahman (M’10) was born in K. Terengganu, Malaysia, in 1986. He received the M.Eng. and Ph.D. degrees in electrical engineering from the University of Southampton, UK in 2010 and 2014 respectively. Currently, he is Senior Lecturer at Universiti Tenaga Nasional (UNITEN), Malaysia. His research interests include high voltage, partial discharge measurement and transformer condition monitoring.

Paolo Rapisarda got a Laurea (M.Sc.) degree in Computer Science at the University of Udine, Italy; and a Ph.D. in mathematics at the University of Groningen, The Netherlands. Currently he is Senior Lecturer at the Information: Signals, Images, Systems group of the School of Electronics and Computer Science of the University of Southampton, United Kingdom.

Paul L. Lewin (M'05-SM'08-F’13) was born in Ilford, Essex, U.K., in 1964. He received the B.Sc. (Hons.) and Ph.D. degrees in electrical engineering from the University of Southampton, Southampton, U.K., in 1986 and 1994, respectively and is currently Professor of Electrical Power Engineering and the Director of the Tony Davies High Voltage Laboratory, University of Southampton. He is the 2016 President of the IEEE Dielectrics and Electrical Insulation Society.