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Department of Electrical and Computer Engineering, University of Cyprus. 75 Kallipoleos Avenue, Nicosia, 1678, Cyprus. 2Department of Mechanical and ...
2007 International Conference on Solid Dielectrics, Winchester, UK, July 8-13, 2007

Classification of Partial Discharge Signals using Probabilistic Neural Network Demetres Evagorou , Andreas Kyprianou 2, Paul L. Lewin 3, Andreas Stavrou 4, Venizelos Efthymiou 4 and George E. Georghiou 1 Department of Electrical and Computer Engineering, University of Cyprus 75 Kallipoleos Avenue, Nicosia, 1678, Cyprus 2Department of Mechanical and Manufacturing Engineering, University of Cyprus, 75 Kallipoleos Avenue, Nicosia, 1678, Cyprus 3 The Tony Davies High Voltage Laboratory, School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom Authority of Cyprus, Nicosia, Cyprus 4Electricity * E-mail: demetres76gucy.ac.cy

and sources. Therefore being able to identify the type and source of the PD is of interest to the HV engineering community. An excellent overview of the different methods used can be found in [2].

Abstract: Partial Discharge (PD) classification in power cables and high voltage equipment is essential in evaluating the severity of the damage in the insulation. In this paper, the Probabilistic Neural Network (PNN) method is used to classify the PDs. After the algorithm has been trained it uses the input vector, which contains the features that would be used for classification, to calculate the probability density function (pdf) of each class and together with the assignment of a cost for a misclassification the decision that minimizes the expected risk is taken. The maximum likelihood training is employed here. The success of this particular method for classification is asserted. This method has the advantage over Multilayer Neural Network that it gives rapid training speed, guaranteed convergence to a Bayes classifier if enough training examples are provided (i.e. it approaches Bayes optimality), incremental training which is fast (i.e. additionally provided training examples can be incorporated without difficulties) and robustness to noisy examples. The results obtained here (99.3°O, 84.3% and 85.5% for the corona, the floating in oil and the internal discharges respectively) are very encouraging for the use of PNN in PD classification.

Identification of different PD sources is by no means an easy task and can be considered as a two step process. The first step in any recognition problem is to consider the problem of what discriminatory features to select and how to extract these features from the patterns. Broadly speaking, there are three different categories of PD pulse data patterns; phase-resolved

data, time resolved data and data having neither phase nor time information. In particular the phase resolved data quantify each of the PD pulses by their discharge magnitude (q), the corresponding phase angle or discharge epoch ((p) at which they occur and their number densities or discharge rates (n). Statistical methods are mainly used for phase-resolved data patterns [3]. Statistical analysis is used to calculate several statistical moments of a univariate distribution. Using a wideband PD detector and a fast enough data acquisition unit the pulse characteristics in the time domain can be acquired. Parameters such as the pulse rise time, pulse decay time, pulse width and the area enclosed under the q-t curve between the rise and the fall time have been used as feature extraction parameters [4-6]. Recently, several signal processing techniques have been applied for the extraction of feature parameters of PD. These include the Fourier, Wavelet, Haar and Walsh transforms the mostly used ones being the Fourier and the Wavelet transforms. A type of feature vector using the Fourier transform consisting of two components has been constructed in [7]. The feature extractor function is a mapping tool that provides a compact meaningful representation of the measured pulses.

INTRODUCTION Achieving reliable and uninterrupted operation in today's transmission and distribution equipment is crucial. In a competitive environment, high voltage systems (HV) have to operate with less downtime and lower maintenance cost. A mechanism for the prediction of system ageing is partial discharge (PD) monitoring. This degradation stress mechanism is a precursor of insulation failure and is defined as an electric discharge that only partially bridges the insulation [1]. Although it does not indicate equipment failure in itself it is a revelation of a weak point in the system. Condition monitoring of HV equipment relies heavily on the detection of PDs. Past methods for PD testing involved the de-energisation of the equipment which is a costly operation both in terms of downtime and effort. Online condition monitoring provides the alternative solution which is gaining increasing popularity. Insulation failure can result from a number of different PD types

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The second stage is that of a good classifier. There is quite a good number of classifiers available in the literature for pattern recognition [8]. The various approaches are based on decision, distance and likelihood functions artificial neural networks [9], trainable classifiers, fuzzy and neuro-fuzzy. A much

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Type Corona Corona Corona Corona Floating Internal Corona Corona

Table 1: Details of generated PD data Voltage No of cycles Sampling Rate 7 kV 500 500kS/s 8 kV 500 500kS/s 9 kV 500 500kS/s 11 kV 500 500kS/s 23 kV 500 500kS/s 22 kV 500 500kS/s 7 kV 500 500kS/s 500 8 kV 500kS/s

Sample length (per cycle) 10000 pts 10000 pts 10000 pts 10000 pts 10000 pts 10000 pts 10000 pts 10000 pts

the Wavelet Transform [12]. At each phase window the number of PDs, the mean amplitude and the maximum values were calculated and plotted for the three types of discharges. The number of PD pulses distribution (Figure 1) and the mean amplitude pulse height distribution (Figure 2) are distinct from the other two in the sense that the corona occurs only around the peak of the negative part of the voltage cycle. The number of discharges for the internal discharge is more or less the same at the peaks of the positive and negative of the

more recent topic used in the identification of PD patterns is the so called machine learning method and in particular the Support Vector Machine (SVM). The SVM uses the concept of Kernels for a number of learning tasks and has shown better performance than neural networks in a variety of fields [10-11]. The SVM is a method for finding functions from a set of labelled training data. Results of using SVM for several feature extractions have been published in [10]. In this paper, the Probabilistic Neural Network (PNN) method is used to classify the PDs. This method is based on the estimation of the pdf of the underlying data that belong to a particular class. After the algorithm has been trained it uses the input vector, which contains the features that would be used for classification, to assign a cost for a particular classification decision. The input data is then assigned to the class that achieves the lower expected risk to this classification decision. The maximum likelihood training is employed here giving the advantage that it reduces the number of hidden layers needed in the Neural Network (NN). As a result the PNN has the advantage over the Multilayer Neural Network that it gives rapid training speed and can achieve guaranteed convergence to a Bayes classifier if enough training examples are provided. The success of this particular method for classification is asserted in this paper. The training data have been obtained from experimental measurements for corona in air, floating in oil and internal discharges.

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The phase information approach was adopted in this the measurements were taken using a low sampling method. The measurements were obtained using a Tektronix Digital Signal Oscilloscope (DSO) with the sampling rate set at 5OOkS/s and a High Frequency Current Transformer (HFCT) with a bandwidth of 300MHz. Three types of artificial PD sources were applied to generate signals (Table 1):

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Figure 2: Mean PD amplitude distribution for discharge at 8 kV for 300 cycles.

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Figure 3: Distribution of the number of PDs for internal discharge at 22 kV for 300 cycles.

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Figure 5: Distribution of the number of PDs for floating discharge in oil at 23 kV for 300 cycles.

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Figure 6: Mean PD amplitude distribution for floating discharge in oil at 23 kV for 300 cycles.

voltage cycle (Figure 3) while the floating discharge has many more discharges occurring at the negative peak of the cycle (Figure 5). Furthermore the mean amplitude pulse height distribution of the internal discharge (Figure 4) gives much higher mean amplitude discharges than the floating discharge in oil (Figure 6).

for input in the NN was therefore a 15 feature set. This is the fingerprint which will be used to identify the different PD types. N

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From the plots of the discharge pulse epoch distribution, the mean and the maximum pulse height distribution, the feature vector was extracted by statistical methods. The skewness and kurtosis were calculated for each of the discharge pulse epoch, the mean pulse height, and the maximum pulse height distribution for the positive and negative part of the cycle. These applied to the three distributions i.e. the discharge pulse epoch, the mean pulse height, and the maximum pulse height, for each type of discharge gave a feature vector of twelve. In addition charge asymmetry was applied to the number of discharge pulses, the mean and the maximum pulse height epoch distribution, generating 3 features. The feature set used

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surface defined by (5) can be arbitrarily complex, since there is no restriction on the densities except for the conditions that all the pdfs must satisfy. The key to using (4) is the ability to estimate pdfs based on training patterns. According to [15-16], the multivariate estimates can be expressed as in

statistical parameters where Q and QX are the sum of the distribution in the positive and negative half of the voltage cycle, respectively; N+ and N are the distributions of the number of discharges for the positive and negative half of the voltage cycle, respectively. In the above calculations N is the number of phase windows in a half cycle (negative or positive only). Skewness and kurtosis are evaluated with respect to a normal distribution. Skewness is a measure of asymmetry or degree of tilt of the data with respect to the normal distribution. If the distribution is symmetric then Sk=O, if it is asymmetric to the left Sk>O and if it is asymmetric to the right SkO, and if it is flatter, K-1