Partial Discharge Classification Using Novel Parameters and a Combined PCA and MLP Technique

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1 Partial Discharge Classification Using Novel Parameters and a Combined PCA and MLP Technique C. Chang and Q. Su Center for Electrical Power Engineering Monash University, Clayton VIC 3168 Australia Abstract: Partial discharge (PD) detection is an important means of testing high voltage equipment because it provides important information about the condition of the insulation system. For condition monitoring purposes, it is vital to identify the type of defects when monitoring discharge activities inside an insulation system. With the help of a new PD measuring and analyzing system, PD source identification using multidimensional feature vector is regarded as a necessary step for the diagnosis of insulation condition. In this paper, new fingerprints derived from physically related PD parameters are presented and fed into a hybrid neural network (NN) for classification. It is proved that the output of a principal component analysis (PCA) network connected to a multi-layer perceptron network (MLP) with backpropagation training algorithm can work successfully with the new fingerprints. 1. INTRODUCTION PDs are localized electrical discharges that behave as a sequence of electrical stress concentrations in insulation material or on the surface of the insulation. They can be detected as electrical pulses having duration of nanoseconds. It is understood that PD distribution patterns are characterized by phase angle ϕ and discharge magnitude θ. In another word, PD distribution patterns include these two parameters [1,, and 1]. The distribution of discharge power loss, which is accumulated PD pulse energy during a chosen reference time interval, can be measured at the terminal of a test object due to the apparent charge magnitudes. The distribution can be derived from the ϕ-θ plane as shown in figure 1. (a) (b) Figure 1 distribution patterns of ϕ-θ-p (a) Air corona from a rod-to-plane arrangement (b) PD from an epoxy resin stator bar sample (c) PD from an oil-impregnated pressboard sample (c) Recently, PD source identification has been carried out in a way of extracting fingerprints primarily from patterns of phase and pulse height resolved PD (PRPD, PHPD) distributions. The limitation of this approach is mainly due to (1) PRPD and PHPD distribution patterns are difficult to be directly associated with the physical process inside an insulation defect. () Some important properties that related to PD pulse-to-pulse characteristics may be missed [,3]. The pulse-to-pulse sequence analysis is based on the fact that a PD event in a sequence is not independent due to the impact from the remaining residuals produced by previous PD events. The reliable identification of discharge source is likely to be successful if physically related discharge fingerprints are provided. To understand the physical basis of PD transients, Pedersen etc. [3,4] studied the fundamental theory of induced charge in an ellipsoidal void. Having adopted a dipole representation, the influence of void parameters upon the induced charge has also been studied. It indicates that the induced charge is dependent on void location, geometry, field orientation, and type

2 of gas, gas pressure, as well as the permittivity of bulk dielectric [5-7]. Specifically, the induced charge can be divided into two components: (1) component q is directly related to the space µ charge in a void and () component q represents p the charge associated with the change in dielectric polarization. Hence, it is appropriate to investigate the discharge patterns that composed of different format in terms of discharge magnitude, voltage and time separation between consecutive PD events. Consequently, fingerprints extracted from V M T ( VMT) distribution patterns become meaningful due to the co-relationship with the physical process inside a defect. The distribution patterns of VMT represent different PD source has shown in figure. local learning rules to build the largest and second largest etc. principal components. The extracted fingerprints from the VMT patterns will be reconstructed by PCA procedure. The output of that will be fed into a MLP network for training.. DISCHARGE MEASURING SYSTEM The test circuit is set up according to figure 3. The applied voltage was started from its inception level and then increased gradually. Using a new discharge detector, PD pulses were detected when they beyond the threshold level. PD pulses were measured in the bandwidth of 1KHz-MHz. HF Filter U~ Ck Ca CD CC PDD Personal Computer (a) Figure 3PD measuring system U~ high-voltage supply CC connecting cable CD coupling device and preamplifier Ck test object Ca coupling capacitor. PDD partial discharge detector (b) 3. CONSECUTIVE DISCHARGES Apparently, comparing with PRPD and PHPD patterns, discharge patterns derived from voltage and charge differences as well as the time separation between consecutive pulses exhibit a characteristic distribution that more related to the physical mechanism of a defect. (c) Figure. distribution patterns of VMT (a) Air corona from a rod-to-plane arrangement (b) PD from a epoxy resin stator bar sample (c) PD from an oil-impregnated pressboard sample PCA is a statistical procedure that can project a very large dimensional data onto a smaller dimensional space while preserving the maximum variance about the input data. PCA can be implemented with As discussed earlier that a propriety interpretation of the induced charge produced in a single void would require the knowledge of the void location, geometry and dimensions, gas components and its pressure [8]. For a given void, there is no direct linkage between the size of the void and the magnitude of induced charge. However, it has been proved that the magnitude of induced charge is dependent on the orientation of the void with respect to the external field direction [4]. Therefore it is physically meaningful to use the distribution characteristics of consecutive PD pulses as illustrated in figure 4.

3 PD Difference (pc) PD Magnitude (pc) Discharge Magnitude Change vs. Time Interval Time Separation (ms) Discharge Magnitude vs. Time Interval difference between consecutive pulses is regard as a parameter for the reliable identification of PD source [11, 1]. This is due to the parameter is proportional to the local field change at the defect site, which is necessary to compensate for the superposed space charge field and thus to reattain the inception field after a preceding discharge pulse. Hence, the change of discharge magnitude, applied voltage, and the time interval in consecutive PD events contain the information in regard to the physical properties of discharge mechanism in a void. A matrix scatter plot is shown in figure 5 in which the relationship among T, V, and M of different PD source can be visually examined. (a) VD Time Interval (ms) 1-1 Discharge Magnitude Change vs Voltage Change 1 5 QD PD Difference (pc) TD Voltage Difference (KV) Voltage Change Sequence (b) VD Normalized V Difference (n) QD TD -. 1 Normalized V Difference(n-1) Figure 4 consecutive PD patterns from a rod-toplane arrangement (c) TVM Distribution Patterns VD - -5 Obviously, using TVM pattern to represent discharge characteristics has distinct advantages comparing with other patterns such as PRPD and PHPD patterns. The change of q p denotes the physical process inside the insulation, which can be represented by the change of discharge magnitude between consecutive discharge events, due to q is µ assumed as a constant for a fixed void location. The time separation between consecutive discharge pulses indicates the time required for discharge initiation and development, which is also related to the physical mechanism of discharge [8,9]. Voltage QD TD Figure 5 Scatter matrix plot of TVM pattern (a) PD from a rod-to-plane arrangement (b) PD from an epoxy resin stator bar sample (c) PD from an oil-impregnated pressboard sample

4 Figure 5 has shown the matrix plots that are helpful to examine two-variable relationships among three variables at once. With the matrix plot, it is useful to identify the meaningful relationship in a single graph. The M- T relation is located at the left bottom of the matrix plot and this pattern is chosen for further analysis. The investigation is continued using the marginal distribution in both T and M axes as illustrated in figure 6. One of the main tasks in feature extraction is to find the similarities among the same type of PD source and at the meanwhile to discover the dissimilarities between different type of PD source. As illustrated in the following diagrams from figure 7 to 9, the T- M patterns produced from three different discharge sources are displayed and compared. Figure 7 PD from a rod-plane arrangement Figure 6marginal distribution patterns of T- M from different discharge source 5. FEATURE EXTRACTION The mapping of T- M distribution is quite dissimilar for different types of discharge source as shown in figure 6. With the same type of discharge source, the T- M distribution will also be varied due to the difference in physical properties of void shape, orientation, surface conductivity, gas pressure external stress and the degree of aging etc. Figure 8PD from a pressboard sample

5 As seen from these T- M patterns, the histogram distributions of either T or M are quite different due to the discharge activities are different under different voltage. The relationship of such a change is not easy to describe just by visual examination. Under the arrangement of dividing the entire distribution area into 6 X 6 windows, the fingerprints can be extracted to represent the mapping of T- M pattern. It is understandable that the more number of window used to describe the histogram distribution will obviously produce more accurate results but at the expanse of computing resources. Therefore the arrangement must be based on the balance between the accuracy and the computing power. procedure, which condensed the input data down to 5 principal components. The number of principal components selected was a trade-off between training efficiency and accurate results [13,14]. Figure 1 A combined topology of PCA and MLP The terminating condition in the unsupervised segment was determined first. The termination of the training was controlled by both the maximum epochs and the weights changed by less than the specified value from one epoch to the next. The learning rate of unsupervised learning usually performs better when it starts out high and then gradually decays during training. As for the supervised learning segment, the training terminates when the MSE drops below the specified threshold. The stop criteria were setup on the basis of cross validation data set, which stops the network when the MSE of the cross validation set increases by the threshold specified. It is actually an indication whether the network has begun to overtrain [15]. The error between the network output and the desired one is an important measure for the success of training. The active cost function is defined as: Figure 9PD from an epoxy resin stator bar 6. PD SOURCE CLASSIFICATION With the measuring system shown in figure 3, every acquisition of PD data was performed for a period of 1 second. The network topology is in figure 1 where a PCA is connected to a MLP. The MLP has one hidden layer with 33 neurons and was trained using backpropagation (BP) training algorithm [14]. PCA is an unsupervised linear procedure that finds a set of uncorrelated features from the input. MLP is able to perform nonlinear classification from the calculated principle components. This hybrid arrangement selects the number of principal components extracted from the 6 X 6 input. With this approach, PCA was regard as a data reduction 1 J t) = ( N ( di ( t) yi ( t)) (5.1) i= 1 Here d is the desired output value and y is the actual output value. N is the number of exemplar and t is the time from the beginning of the training. The convergence of the active cost function J (t) of MLP and a combined PCA and MLP are shown in figure 11 and 1 respectively. Compared with a MLP network, a combined PCA and MLP neural network has better performance in terms of PD source classification using data extracted from the mapping of T- M pattern. After being trained, the network calculate the output value according to input data and trained weights. The calculated outputs and the desired outputs are shown in figure 13.

6 presenting results obtained for the identification of insulation defects of different type. 8. REFERENCES Figure 11 Active cost function of a combined neural network during training Figure 1 Active cost function of a MLP neural network during training Comparison between Real and Desired Output Value Real output R Real output R1 Desried output R Desired outpu R1 Examplar Figure 13 Comparison between the calculated and the desired outputs 7. CONCLUSION Partial discharge is a complex phenomenon and the pulse-to-pulse distributions are dependent on defect void location, geometry, surface conductivity, field orientation and applied voltage. Based on the theoretical analysis that the change of discharge magnitude within the consecutive PD events is closely related with the PD proprieties. The derived T- M pattern is practically useful and it is also demonstrated that the successful classification of different discharge source has been achieved. PCA is efficient to extract information globally from input space by projecting a high-dimensional data set to a smaller-dimensional output space. It is can be used as a data preprocessor for MLP. The prospect of this approach has been verified by [1] R. Bartnikas, A Commentary on Partial Discharge Measurement and Detection, IEEE Trans. on Dielectrics and Electrical Insulation, Vol., pp , [1] C. Chang and Q. Su, "Comparison between Pattern Recognition Techniques for Partial Discharge Identification", AUPEC'98, the UT, Hobart, Tasmania, 7-3 Sept. 1998, pp [] C. Chang, Q. Su, Partial Discharge Distribution Pattern Analysis Using Combined Statistical Parameters, IEEE PES Winter Meeting, Singapore, January 4-8, [3] C. Chang and Q. Su, "Statistical Characteristics of Discharges from A Rod-Plane Arrangement", AUPEC'99, the University of Northern Territory, Darwin, 7-9 Sept [4] A. Pedersen On the Electrical Breakdown Gaseous Dielectrics An Engineering Approach, IEEE Trans. on Electrical Insulation, Vol. 4 No. 5, pp , October [5] A. Pedersen, G. C. Crichton, The Theory and Measurement of Partial Discharge Transients, IEEE Trans. on Electrical Insulation, Vol. 6 No. 3, pp , June [6] G. C. Crichton, P. W. Karlsson and A. Pedersen, Partial Discharges in Ellipsoidal and Spheroidal Voids, IEEE Trans. on Electrical Insulation, Vol. 4 No., pp , April [7] A. Pedersen, G. C. Crichton and I. W. McAllister, Partial Discharge Detection: Theoretical and Practical Aspects, IEE Proc-Sci. Meas. Technology, Vol.14, No. 1, pp. 9-36, January [8] R. J. Van Brunt, "Stochastic Properties of Partial- Discharge Phenomena", IEEE Trans. on Dielectrics and Electrical Insulation, Vol. 6, No. 5, October 1991, pp [9] R. J. Van Brunt and E. W. Cernyar, "Importance of Unraveling Memory Propagation Effects in Interpreting Data on Partial Discharge Statistics", IEEE Trans. on Electrical Insulation, Vol. 8 No. 6, pp , December [1] E. Gulski and F. H. Kreuger "Computer-Aided Recognition of Discharge Sources", IEEE Trans. on Electrical Insulation, Vol. 7, No. 1, pp. 8-9, February 199. [11] Martin Hoof, Bernd Freisleben and Rainer Patsch, "PD Source Identification with Novel Discharge Parameters Using Counterpropagation Neural Networks", IEEE Trans. on Dielectrics and Electrical Insulation, Vol. 4 No. 1, pp. 17-3, February [1] Rainer Patsch and Martin Hoof, "Physical Modeling of Partial Discharge Patterns", IEEE Internation Conference on Conduction and Breakdwon in Solid Dielectrics, June -5, 1998, Vasteras, Sweden. [13] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing, 1974 [14] Y. Linde, A. Buzo and R. Gray, An Algorithm for Vector Quantization Design, IEEE Trans. On Communications, COM , 198. [15] Gallant and White, On Learning Derivatives of an Unknown Function with MLPs, Neural Networks 5 (1), ,199.

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