Advanced Approach for Automatic PRPD Pattern Recognition in Monitoring of HV Assets

Alexander Belkov Alija Obralic

Wojciech Koltunowicz Ronald Plath

OMICRON electronics GmbH Lehrter Str. 16-17, 10555 Berlin, Germany

alexander.belkov@omicron.at

Abstract- This paper deals with progress in automatic approach for phase resolved partial discharge (PRPD) pattern recognition to be applied in the monitoring of HV equipment. To define classes of defects generating partial discharges (PD), a combination of scalar and vector features is proposed. The feature generation, extraction, selection and classification based on the comprehensive analysis of PRPD patterns are performed. The most important features are indicated by PD human experts and automatic knowledge extraction methods are applied. In particular, statistics, object and shape analysis, as well as fractals and wavelet analysis are taken into account. For the feature selection and reduction, different linear and non-linear methods are investigated by using supervised cluster analysis. This permits to choose the most relevant features for classification. Several classification schemes including statistical model, linear and nonlinear classifiers are evaluated in order to distinguish PRPD patterns in the feature space. The pattern recognition approach is verified on PRPD patterns derived from laboratory and field PD measurements performed on HV XLPE cables and rotating machines. The correlation with PD human experts' interpretation of such patterns was also evaluated. Keywords-partial discharge; monitoring; pattern recognition

I. INTRODUCTION

Expensive HV apparatus are subjected to aging during their service life. In order to prevent severe failures and extend the lifetime of the equipment, different maintenance policies are applied. There is a clear trend in asset management to change from time-based towards condition-based maintenance. Continuous or periodic monitoring of the insulation condition is considered an essential tool for proper maintenance management in order to guarantee a high level of asset reliability [1]. PD phenomena are consequences of local electrical stress concentrations in the insulation. The large variety of PD signals makes such measurements a challenging task. However, PD measurement is a worldwide accepted method for insulation diagnosis and PD measurements are specified for type and routine testing of most high and medium voltage electrical equipment. PD activity indicates the weak point in the insulation and can lead to failure and consequently to a fault. The introduction of digital technology created new opportunities for improving the sensitivity and significance of PD measurements, by far exceeding the

capabilities of older analog PD measuring systems [2]. PD measuring data is commonly represented as PRPD patterns and various computer aided PD defect identification procedures are applied [3]. Powerful reference data is considered as a key component in identifying the type or nature of a PD defect and distinguish it from noise sources on site [4]. But still PD activity requires intervention by an expert, who has to identify and analyze the PD source. To reduce the cost of such expert services, this contribution presents an advanced automatic approach for PRPD pattern recognition of defects to be applied in the diagnostic and monitoring of HV equipment.

II. PD MONITORING PROCEDURE The advanced concept of continuous PD monitoring system is presented in Fig. 1. The signals from PD sensors are acquired in a multi-channel data acquisition unit. The acquisition unit performs advanced pre-processing of the raw data, to remove disturbances and to determine PD characteristics. The output of the PD data pre-processing is transferred to a server that enables long-term data storage. The duration of PD data acquisition and storage is adjusted to the asset type, its importance and operation experience. Advanced intelligent pre-processing reduces the amount of data to adequate levels for transmission over a communication network. An expert system performs data post-processing

Figure 1. PD continuous monitoring concept

978-1-4244-6301-5/10/$26.00 @2010 IEEE

converting real-time data into useful information about the insulation condition of HV apparatus. The type of the PD defect is identified by means of an automated PRPD pattern recognition system. The main key to perform automatic diagnosis of the state of the insulation is the exact separation of different PD sources and effective suppression of external noise. To achieve this, different techniques can be applied. The most promising are synchronous multi-channel and multi-spectral evaluation techniques.

III. SEPARATION OF MULTIPLE PD SOURCES

A. Synchronous Multi-channel PD Evaluation Technique The new evaluation technique for synchronous multi-channel measurements (3PARD) was originally developed for evaluation of three-phase PD measurements as its application requires three independent PD observers like e.g. capacitive taps of three-phase bushings or coupling capacitors connected to the terminals of generators. It evidenced that crosstalk effects contain important information on PD pulse (and on signal propagation) and it helped to remove disturbances and, moreover, to clearly distinguish between multiple PD sources [2, 5, 6]. The 3PARD (star diagram) visualizes the relation among amplitudes of a single PD pulse in one phase and its crosstalk generated signals in the other two phases. By repetition of this procedure for a large number of PD pulses, PD sources within the test object as well as outer noise appear as clearly distinguishable concentration of dots in a 3PARD diagram. B. Synchronous Multi-spectral PD Evaluation Technique This technique enables synchronous multi-spectral PD measurement (3CFRD) to separate PD sources. In contrast to 3PARD, 3CFRD evaluation is performed on a single channel PD measurement. Multi-spectral measurements are based on pulse spectra correlation by applying three different band-pass filters simultaneously. The three band-pass filters can be tuned to areas with low interference level. Through proper choice of the band pass center frequencies, it becomes feasible to perform PD measurements according to the IEC 60270 standard and at the same time remove practically all superimposed disturbances. The 3CFRD correlates the output of the three band pass filters exactly like 3PARD does with the pulse magnitudes of a PD triple [2, 5, 6]. Both, 3PARD and 3CFRD diagrams show different pulse-type sources in separable clusters and each cluster is selectable for showing the PRPD pattern of a single PD source only without superimposition effects (Fig.2).

IV PD PATTERN RECOGNITION PROCEDURE A. Pattern recognition architecture

PRPD pattern recognition aims to classify PD defects in order to identify the nature of a PD source. In Fig.3, two modes of PD defect classification using PRPD patterns are

Figure 2. An example of separation of PD sources by 3PARD shown: supervised learning as a preprocessing mode and classification as an operation mode. The supervised learning is performed in advance to prepare for the classification of unknown samples [7]. It is based on the reference database which contains PRPD patterns labeled with classes. The result is learning information which contains the setup data for the classifier. The content of the setup data depends on the classifier itself. The learning mode can be repeated to train the system for new features, a new classifier or for an updated reference database. Classification is applied for each unknown sample [6]. It contains the same feature extraction and feature transformation steps which were used during the learning mode and uses a classifier which is initialized with the derived learning information. After classification, the sample is assigned to a certain class or is rejected by the classification. The effectiveness of the classification is highly dependent on the algorithms and methods implemented in each step. An exhaustive comparative analysis was performed on numerous algorithms to optimize the classification performance. The most appropriate algorithms and effective performance evaluation procedures are applied in each step of a PD defect classification. The flowchart for the advanced PRPD pattern recognition is shown in Fig. 4.

Figure 3. PD defect classification modes

Figure 4. Flowchart for the advanced PRPD pattern recognition

B. PRPD Reference Database

The reference database contains PRPD patterns obtained on site and in the laboratory, under well defined measuring conditions. In the laboratory, several measurement parameters like e.g. test voltage, sensor type, set-up geometry and configuration were changed to expand the database content. Only the PRPD patterns acquired from well-defined PD sources detected on site were considered. Variations of discharge magnitude and shape of PRPD patterns in relation to the magnitude and phase angle of the applied AC voltage, as well as to the type of insulation material and to the location of the PD source were taken into account. The database comprises several PD discharge classes like e.g. surface, corona and inner discharges, and principal disturbance type classes, each containing a number of patterns from different setup configurations. The principal forms of disturbances are interference from the mains and from the earthing system, electromagnetic radiation, discharges in the test circuit and contact noise. The PRPD reference database is defined as

}{:. ji CIdbref ∈ (1) where: iI is a PRPD pattern, Mi ..1= ( M is the number of PRPD patterns), jC is a class (PD defect) for PRPD pattern and Nj ..1= ( N is the number of classes for PRPD patterns) The PRPD patterns are represented as 2D numerical matrices [ ]500400 × . Each cell of the matrix contains information about the number of PD pulses, their charge level and about their phase correlation to the applied AC voltage. C. Feature Generation

Feature generation is a preliminary step for feature extraction, where PD experts formulate, as text descriptions, important features to clearly distinguish between different PD defects on PRPD patterns. The following PRPD distributions are taken into account by the experts:

• Hphi ( t~ϕ ): phase angle vs. time; • Hq ( tq ~ ): charge vs. time • Hn ( qn ~ ): number of impulses vs. phase angle;

• Hqn ( ϕ~aq ): average charge vs. phase angle; • Fn ( qa ~ϕ ): average phase angle vs. charge.

To distinguish PD behavior during the visual interpretation of PRPD patterns, the expert evaluates e.g.: the appearance of objects (concentration of PD pulses) on both half cycles of the applied AC voltage, size, position and shape of the objects. D. Feature Extraction Feature extraction aims to replace text descriptions of PRPD patterns from the experts with related numerical features

ii xIextrfeat →:.. (2) where: [ ]niiii xxxx ,,, 21 …= is a feature vector for the PRPD pattern, n is the number of features. For this scope, effective computation algorithms are chosen that combine modern feature extraction methods with the authors computational approaches. Appropriate statistical operators are applied for characterization of the Hphi, Hq, Hn, Hqn and Fn distributions: arithmetic mean, standard deviation, mode, median, low quantile and high quantile, skewness, kurtosis, number of peaks of the distribution. Additionally, specific operators like cross correlation factor, discharge factor, phase asymmetry are used for the Hqn distribution. Most of the statistical operators are calculated separately for the positive and negative half cycles of the applied AC voltage. To express object properties of PRPD patterns in a numerical representation the binarization procedure was used. It transforms a PRPD pattern treated as a grayscale image into a binary pattern and allows forming more clearly separated objects. Several image processing approaches for binarization including global and local thresholding, region splitting and merging, clusterization were implemented and analyzed [8]. Further, object analysis is performed on binary PRPD patterns. Implemented numerical object features include similarity and symmetry of positive and negative half cycles of the applied AC voltage, the number of objects on each half cycle, orientation (to characterize the main directions of objects), granulometry (an approach to compute a size distribution of objects), fractal features, shape.

Wavelet analysis is additionally used for object analysis on PRPD pattern as a powerful method to describe and distinguish even overlapped objects [9]. It allows accumulating specific characteristics (charge and phase changes) for different PD defects by analyzing and comparing horizontal and vertical variations in both half cycles of a PRPD pattern. Implemented wavelet features include wavelet phase averaging and wavelet charge averaging separately for each half cycle of the applied AC voltage. In total 118 ( 118=n ) of PRPD pattern features coming from statistical, object and wavelet analyses were taken into account. The values of all features were normalized. Normalization prevents certain features from dominating distance calculations (used in pattern classification) because they have large numerical numbers

Rxxnorm ii },ˆ{}{:. → (3)

where: [ ]niiii xxxx ˆ,,ˆ,ˆˆ 21 …= is a normalized feature vector for the PRPD pattern where each coordinate [ ]1..0ˆ ∈lix , R is a feature normalization model. E. Feature Selection The large amount of features can increase the computational complexity and decrease the accuracy of pattern classification (the phenomenon called 'curse of dimensionality'). In order to eliminate this, feature selection is applied. It is a procedure to reduce the number of features and at the same time keep as much as possible their class discriminatory power [7]. The selected features lead to large inter-class distance (better separation between classes of PD defects) and to small intra-class variance (PRPD patterns which belong to one class are placed close to each other) in the feature space. Formally this represented by (4) or (5)

Syxselectfeat ii },{}ˆ{:.. 1 → (4)

SyCxselectfeat iji },{},ˆ{:.. 2 → (5)

where: [ ]kiiii yyyy ,,, 21 …= is a reduced feature vector for the PRPD pattern, k is the number of reduced features ( nk << ) and S is a feature selection model. The definition (5) differs from (4) by taking into consideration the correspondence ji CI ∈ between PRPD patterns and classes of PD defects. Several linear and non-linear feature selection methods were analyzed to choose the optimal one in the application to PRPD patterns and to the classes of PD defects. The tested methods include Principal Component Analysis (PCA), Kernel PCA, Independent Component Analysis (ICA) which correspond to (4), as well as Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (GDA) which correspond to (5) [7]. The comparative analysis of the methods was done by using supervised cluster analysis, especially class separability measures (scatter criteria) in the reduced feature space }{ iy [7]. The comparative analysis consists of finding the extrema of the scatter criteria and the corresponding feature dimension.

Good results for the separation of pattern classes in the feature space were achieved with LDA ( 5=k ) and PCA ( 10=k ). The ratio of class intraset-to-interset distances for both methods is below the value 1 which means that classes do not overlap each other in the multidimensional feature space (Fig. 5).

Figure 5. Ratio of cluster intraset-to-interset distances LDA showed a better class distribution ability in the feature space than PCA (Fig. 6). The LDA feature selection model S is very sensitive to patterns with class labels. In order to be stable, a large number of classified patterns should be taken into account for LDA. Therefore only PCA with 10=k was selected as the most proper feature selection method.

Figure 6. 2D representation of the multidimensional feature space reduced by PCA

The PCA feature selection model S is a linear orthonormal projection:

bxWy iT

i += ˆ (6) where: the matrix [ ]knW × and the vector [ ]1×kb are parameters of the projection. PCA, as a linear feature selection method, allowed estimating how effective the set of n features is for PRPD patterns and which of them are more important than others, which are useless or redundant for the task of pattern classification. The matrix W describes linear transformations of the initial feature set to a reduced one. Each coordinate in iy after feature selection is a linear combination of all initial features from ix̂

nitnititti xwxwxwy ˆˆˆ 2211 +++= … (7) where: }{w are coefficients of the transposed matrix W and

kt ..1= . Relative weights of n features as indicators of their importance for pattern classification were calculated based on coefficients of the matrix W of linear transformation for PCA.

F. Pattern Classification Pattern classification allows distinguishing PRPD patterns of different classes in a multidimensional feature space. The goal of classification is to assign an unknown PRPD sample to a class (PD defect), or to reject the sample for the classification. Formally it can be represented in two ways:

Cyclasspatt ′→′:.. 1 (8)

{ }jj pCyclasspatt ,:.. 2 →′ (9)

where: y′ is a reduced feature vector for a PRPD sample I ′ in accordance with the feature selection model yxS ′→′ˆ: , x′ˆ is a normalized feature vector for I ′ in accordance with the feature normalization model xxR ′→′ ˆ: , x′ is a feature vector for I ′ , C′ is a class (PD defect) for PRPD sample, { }jj pC , is a set of pairs where each class jC is provided

with a probability [ ]1..0∈jp , Nj ..1= . Based on the analytic analysis specific to PD pattern recognition, several most challenging pattern classification methods were selected for further investigation. These methods are k-nearest neighbour classifier (k-NN), nonlinear multi-class support vector machines (SVM) including in particular One-Against-All (OAA), One-Against-One (OAO) decompositions of SVM [7, 10]. The k-NN classifier, definition (9), provides probabilities of classes for a sample and does not require at all the training stage (supervised learning). The SVMs have better generalization properties and yield more stable recognition results for the training set. Besides, SVMs are independent of the number of features for pattern classification. They allow us to use high-dimensional feature space for recognition without dimensional restrictions. The selected SVMs correspond to the definition (8) but the OAO SVM provides votes for each class, instead of class probabilities. Besides k-NN and SVMs, the multi-class linear classifier (LC) trained by the perceptron was also selected because the feature space is optimized by using the feature selection procedure and classes of patterns can be already linearly separable. G. Results The pattern classifiers chosen in the previous chapter were trained and tested to classify PRPD samples for different PD defects (general PD identification). Different settings for the classifiers were evaluated, as they strongly influence the classification performance. Ten features were calculated by PCA to be the best separators of the classes as inputs for the classifiers. In total more than one hundred features derived from PRPD patterns were taken into account. The contributions of features to effective pattern classification were also defined. The classifiers were tested with different settings by using cross validation on the PRPD reference database. Optimal settings were defined for the classifiers by the comparative analysis of the recognition results. The best recognition rate was achieved for SVM (OAO multi-class

decomposition of SVM). Lower rates were obtained for other SVMs and k-NN. LC did not provide satisfying recognition results. This indicates that the classes of PRPD patterns in the feature space have no linear separation. In the next step, the application of a binary tree architecture of SVM utilizing a decision tree is planned. In this case, the PD pattern recognition will be a sequence procedure of allocated sub classifications. It will lead to a reliable and more transparent multilevel decision-based approach. All computational algorithms including feature extraction, feature selection, pattern classification and performance evaluation procedures were performed in MATLAB.

V. CONCLUSIONS

Automatic identification of PD defects requires the effective separation of PD and possible superimposed noise or multi-source PD. The synchronous multi-channel (3PARD) and multi-spectral (3CFRD) evaluation techniques were successfully applied to fulfil this requirement;

More than one hundred features of PRPD patterns coming from statistical, object and wavelet analyses were taken into account. Their contributions to effective pattern classification were defined by using supervised cluster analysis;

A new feature extraction approach for object properties of PRPD patterns was proposed and successfully used;

The best PRPD pattern recognition rate was achieved for SVM (OAO multi class decomposition of SVM).

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