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A. K. Chaou et al.: Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition

Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition on HV Polluted Insulator Model A. K. Chaou, A. Mekhaldi and M. Teguar Laboratoire de Recherche en Electrotechnique Ecole Nationale Polytechnique d’Alger 10 Avenue Hassen Badi, B.P 182, El-Harrach, 16200 Algiers, Algeria.

ABSTRACT Insulator flashover under pollution is one of the most important problems for power transmission. Occurrence of flashover is preceded by discharges propagation. This paper is dedicated to monitor discharges activity through arcing discharges pattern recognition using a combination of efficient image processing and classification algorithms. Images are extracted from recorded videos of flashover process over a plane model insulator under various contamination levels. Then, an algorithm is proposed and tested over a large image database. This algorithm processes in four stages. First, Otsu image segmentation algorithm is initially applied on images. Next, morphological filtering by combining erosion and dilation operations is computed to eliminate unwanted noises such as light reflections on the insulator model. Afterwards, connected components on filtered image are labelled enabling the calculations of four important morphological indicators consisting in the number of the connected labeled components ( ) and the number of pixels, the length and the width of the largest connected component region ( , L and W respectively). These indicators characterize different properties of discharges activity and are used as an input of three well know classification algorithms (Knn, Naïve Bayes, Support Vector Machines) to distinguish between the presence or not of arcing discharges on the insulator surface. This paper introduces image processing as an efficient and fast tool for discharges activity analysis and insulator flashover monitoring. The proposed methodology dispenses the heavy instrumentations and tedious processing of conventional laboratory tests. Index Terms - Flashover, insulator pollution, arcing discharge, Otsu method, morphological filtering, connected components labelling, pattern classification.

1 INTRODUCTION NOWADAYS, due to the combination of growing demand for electricity and the need to upgrade or replace existing equipment of the electrical network, massive investments will be required to meet future needs. Such investments are meaningless if efforts are not consented to assure security of such equipment. Indeed, the first challenge consists in transmitting electricity (covering the distances between producer and consumer). During this transmission process, insulators play a primary role by maintaining electrical insulation ranging from distribution to transmission lines and supporting mechanical load between a conductor and the ground. However, transmission and distribution systems remain vulnerable to many faults. One of the most harming is the electrical flashover of insulators under pollution. In order to minimize risks of flashover occurrence on high voltage insulators and assure safety of equipment, operators and Manuscript received on 5 January 2014, in final form 3 August 2014, accepted 18 September 2014.

users, the flashover phenomenon has to be thoroughly studied. The major cause of this phenomenon is the accumulation of pollution on the insulator surface. Many research reported that pollution layer distribution is generally non-uniform [1-8]. M. A. Douar et al [4-7] analyzed the flashover process on a plane insulating surface under non-uniform pollution and examined the frequency characteristics of the leakage current (LC). The measurements of phase angle between LC and applied voltage indicate that the equivalent impedance of the insulator could be simulated by Resistor-Capacitor (RC) circuit with a high capacitive effect engendered by the preestablished clean band. This effect decreases with the appearance of electric discharges. Using Standard Deviation Multi Resolution Analysis (STD-MRA) representation of LC, authors reported good correlation between the insulator surface state and details of LC obtained through Discrete Wavelet Transform (DWT) decomposition. Recently, B. Moula et al [8] used this same technique to detect the eventual presence of partial arcs activity over non-uniformly polluted glass surface. A group of authors introduces Acoustic Emission (AE) as a novel discharges monitoring method on insulators under

DOI 10.1109/TDEI.2014.004549

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contamination conditions. Li et al [9, 10] proposed the AE method to characterize partial discharges (PD) during the whole process of flashover. Besides, C. M. Pei et al [11,12] suggested AE to assess insulator surface contamination combined with Least Square Support Vector Machine (LSSVM). Two papers authored by B. X. Du et al [13] and Y. Liu et al [14] introduce Recurrent Plot (RP) technique to monitor insulator performance through LC analysis on outdoor and rime-iced composite insulator. These studies concluded that propagation and properties of the discharges can be graphically projected on the topological structure of RP. Thus, RP is believed to be an efficient technique to study discharge states during the flashover process. For many researchers, insulator performance monitoring goes through level contamination prediction based on LC examination. Hence, to predict ESDD value, Li et al [15] used the mean value ( ), maximum value ( ) and standard deviation of LC using Artificial Neural Network (ANN). On the other hand, Jiang et al [16] combined ANN with fuzzy logic having as input the LC peak ( ), the phase difference ( ) between LC and applied voltage and the total harmonic distortion (THD). Investigations of performance of high voltage insulators through LC waveforms classification were accomplished by two relevant studies carried out by Pylarinos et al [17-18]. In the first one, authors proposed 20 features from time and frequency domains, and compared classification performance of LC waveforms using three algorithms (Knn, Naïve Bayes, Support Vector Machines). Features from frequency domain provides better results compared to time domain on one hand and both frequency and time domains on other hand. In the second study, STD-MRA based on DWT was used to extract features from LC and an ANN to classify the degree of hydrophobicity loss on a polymer coated insulator surface. All previously cited studies concerning the flashover monitoring and high voltage insulators performance were based on the acquisition of LC. Such acquisition involves a prominent laboratory test arrangement, including an AC capacitive voltage divider and large bandwidth digital oscilloscope. The novelty of our present study is to monitor flashover and investigate insulator performance through an image processing methodology including segmentation, filtering, feature vector computation and classification. This methodology dispenses the heavy instrumentations and tedious processing of conventional laboratory tests. Segmentation is the crucial step in image processing. It consists in partitioning an image into regions of interest in order to extract features from those regions. Therefore, accurate and robust segmentation are important to obtain an efficient pattern recognition methodology. In the medical domain, Otsu's thresholding used as segmentation method is mandatory to achieve correct clinical diagnosis [20-23]. Referring to many studies, Otsu technique considered as the most suitable image segmentation method for brain tumor from a Magnetic Resonance Image [24], lung cancer [25] and mass cells [26]. Hence, due to the efficiency of this segmentation technique allowing the detection of malignant

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tumors in the medical domain, it will be employed in this work to detect and study properties of electrical discharges on insulating surface. After the segmentation step, morphological filtering is used to eliminate light reflections considered as noises in this work. Then, the connected component labelling process is applied enabling the study of discharges activity. Finally, arcing discharges are detected using supervised learning classification algorithms. Based on experimental tests carried on an insulator model, this study aims to monitor flashover, investigate surface electrical activity and diagnose insulator performance. To achieve this work, a digital camera is used to record appearance and elongation of discharges until flashover. Images are extracted from those videos. To investigate discharge activity, we propose a novel image processing algorithm. This algorithm aims to extract surface discharges properties and recognize arcing discharge pattern through four proposed indicators computed based on the filtered images. These indicators concern the number of the connected labeled components ( ) and the number of pixels, the length and the width of the largest connected component region ( , L and W respectively). They quantify very interesting phenomena and are used as input of three well-known classification algorithms: Knn, Naïve Bayes and Support Vector Machines. This paper brings a significant contribution through a proposed image processing algorithm to investigate discharges activity on an insulating surface. Methodology described in this paper can be extended, through video surveillance technique, to power distribution system monitoring (DSM).

2

EXPERIMENTAL SETUP

Experiments were carried out using a high voltage test transformer (300 kV/50 kVA, 50 Hz) supplied by a regulating transformer (220/500 V, 50 kVA, 50 Hz). The laboratory model was constituted by a glass plate (500 mm x 500 mm x 5 mm). Made up of aluminum paper, two electrodes (500 mm x 30 mm x 0.003 mm) have been used on both High Voltage (HV) and ground sides as shown in Figure 1. The distance between the two electrodes represents the leakage path of the 1512 L cap and pin insulator (292 mm), which is largely used by the Algerian company of Gas and Electric Power (SONELGAZ). This glass model is placed at 192 cm height from the ground on two rubber tubes which were installed on a wooden table.

Figure 1. Laboratory plane model.

A SONY DCR-SR video camera is used to record discharges development and flashover phenomena, as well as a personnel computer to videos acquisition (Figure 2).

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A. K. Chaou et al.: Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition

(a) Stage 1 (4 kVrms)

(b) Stage 2 (6 kVrms)

(c) Stage 3 (15 kVrms)

(d) Stage 4 (20 kVrms)

(e) Stage 5 (24 kVrms)

(f) Stage 6 (28 kVrms)

H.V.T: High Voltage Transformer, R.T: Regulating Transformer I.T: Isolating Transformer, V.R: Voltage Regulator V.C: Video Camera, T.S: Test Sample

Figure 2. Laboratory test arrangement.

The contaminating pollution is composed of distilled water and NaCl and is uniformly and continuously spread all over the model surface. To ensure good reproducibility of the pollution, the saline solution is pulverized 25 times on each side of the insulator model using a sprayer placed above this model and at a distance of 50 cm from it, as shown in Figure 3. The pollution conductivity values adopted are: 0.01, 0.19, 0.71, 1.2 and 10.1 mS/cm.

Pulverization x25 50 cm

Figure 3. Pulverization method of the polluted solution on the insulator model.

(g) Stage 7 (30 kVrms)

3 FLASHOVER STAGES

Referring to [16] presenting typical discharges phenomena, we propose a classification of different steps of flashover summarized in Table 1. Table 1. Typical discharge phenomena preceding flashover.

Typical discharge phenomena No obvious arc discharge Weak purple Spark Purple discharge in the shape of brushes Short local arc discharge Dense small arc discharge Bright main arc discharge Intensive red main arc discharge

Class No arc discharge

Stage 1 2 3 4 5 6 7

Arc discharge

In order to monitor the flashover phenomenon on a plane insulator model, activities preceding this critical discharge have to be analyzed and studied thoroughly. Hence, using the experimental arrangement depicted in Figure 2, for a conductivity equal to 1.2 mS/cm, the applied voltage is increased slowly from 0 kVrms until the flashover voltage (32 kVrms) with a rate approximately equal to 2 kVrms/s. Results are presented in Figure 4. For magnitudes of the applied voltage less than 3 kVrms, no discharge is observed. However, at 4 kVrms, discharges appear at both HV and ground electrodes sides (Figure 4.a). Increasing the applied voltage to 6 kVrms, weak purple sparks occur over the same areas (Figure 4b). Intense luminosities, representing purple discharges, in shape of brushes, appear at 15 kVrms (Figure 4c). Until this voltage level, there is no observation of obvious or intense arcs on the insulator surface. However, at 20 kVrms, short local arc discharges appear. These arcs, rather being small and localized, are numerous (Figure 4d). At 24 kVrms, two dense but small arc discharges are observed and are surrounded as shown in Figure 4e. The bigger one is at the ground electrode side, and the smaller at HV electrode one. With the increase of applied voltage, these two arcs, which elongate and become more intense (Figures 4f and 4g), lead to the total flashover (Figure 4h).

(h) Flashover discharge (32 kVrms)

Figure 4. Stages of Flashover process.

This classification is based on the appearance of arcing discharges on the insulating surface. Indeed, as presented in Figure 4, for applied voltage levels less than 20 kVrms, we observe only luminosities. These latter are not considered as arcing discharges. Thus, images from Figure 4a to Figure 4c belong to the no arc discharge class. However, for applied voltage levels above 20 kVrms, the significance of the intensity of appearing luminosities allows us to classify images from Figure 4d to Figure 4g in arcing discharge class.

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4 METHODS A brief theoretical background of multiple used methods is presented in this section. 4.1 OTSU SEGMENTATION METHOD Segmentation is often considered to be the first step in image analysis. Its purpose is to subdivide an image into regions, which would be used for further analysis. Image segmentation using thresholding is very popular to extract an object from its background by assigning as threshold value for each pixel. This pixel is either classified as an object point or a background point. In case of a fixed thresholding, value is held constant throughout the image. Thus, assuming that represents the initial image, the thresholded image is binary and given by: ,

0, 1,

(1)

The fixed thresholding method assumes high-intensity pixels are of interest, and low-intensity pixels are meaningless. In other words, it can ignore important details on image. Therefore, for a better image representation, an automated method for threshold selection is more suitable. In this domain, the Otsu algorithm is very convenient. It assumes that the image to be thresholded contains two classes of pixels or bi-modal histogram (foreground and background) then calculates the optimum threshold separating those two classes so that their intra-class variance (within-class variance) is minimal. Thus, the threshold is computed to minimize the intra-class variance, defined as a weighted sum of variances of the two classes, as described by equation (2): (2) where and are variances of the pixels in the background (below threshold) and foreground (above threshold) respectively. and are class probabilities (or weights) and are estimated by equations (3) and (4) respectively:

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Only the optimal threshold maximizes the inter-class variance. can be obtained by much simpler computation , , through recurrence relations based on updating and μ as pixels and the respective cluster means μ move from one cluster to the other as increases, as described by equations (9) to (12): (9) 1 1

(10)

μ

1

(11)

μ

1

(12)

4.2 MORPHOLOGICAL FILTERING Binary image, obtained from Otsu segmentation may contain numerous imperfections due to image noises (the reflections of laboratory lights on the insulator boundaries in our case). Morphological image processing allows to remove these imperfections by taking into account the form and structure of the image. Dilation and Erosion are the basic operations of morphological filtering, and can be combined into more complex sequences but are not invertible operations; if an image is eroded and dilated, the original image is not reobtained. 4.2.1 EROSION The basic idea of erosion is to probe a binary image with a simple shape called structuring element. The obtained image contains 1 in all locations of a structuring element's origin at which the structuring element fits in the input image. Thus, erosion shrinks objects by eroding their boundaries. Let be an Euclidean space or an integer grid, and a binary image in . The erosion of the binary image by the structuring element is defined by: | (13) where

is the translation of

by the vector , i.e: ,

∑

(3)

|

∑

(4)

In other words, assuming that the origin of is at its center. We superimpose the origin of in each pixel of . If is completely contained in , the pixel is retained, else deleted. 4.2.2 DILATION Analogously to erosion, dilation allows objects to expand, by potentially filling small holes and connecting disjoint objects. The dilated image contains 1 in all locations of structuring element's origin at which the structuring element hits the input image. The dilation of by is defined by: | (15)

0, 1 is the range of intensity levels. To compute this inter-class variance, we subtract the intraclass variance from the total variance of the combined distribution to get the inter-class (between-class) variance as shown in equation (5): (5) Replacing equation (2) in (5), we obtain: μ μ where is the combined variance and mean (described by equation (7)). Note variance is the weighted variance of themselves around the overall mean. μ μ

μ μ (6) μ is the combined that the inter-class the cluster means μ

(7)

Substituting equation (7) in (6), we obtain a simpler formulation of the inter-class variance shown in equation (8): μ

μ

(8)

where

denotes the symmetric of |

(14)

and is defined by: (16)

That is to say, if has a center on its origin, then the dilation of by can be understood as the locus of the points covered by when the center of moves inside . Figure 5 shows an example of erosion and dilation on a binary image for a 3x3 square structure element.

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A. K. Chaou et al.: Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition

(b) (b) (c) Figure 5. Morphological filtering example: (a) original image, (b) eroded image of (a) and (c) dilated image of (a).

4.2.3 OPENING Opening, which consists in an erosion followed by a dilation, is used to eliminate all pixels enable to contain the structuring element.

processed to eliminate noises on the segmented image. Third, based on the filtered image, connected components are labeled to calculate four morphological indicators describing the discharge activity on the insulating surface. This step is commonly known as Feature Extraction and is developed in section 5.2. In the fourth and last part, these indicators are the input of three supervised learning classification methods. The flowchart of the proposed algorithm is portrayed in Figure 7. Start

Flashover videos acquisition Image extraction

4.3 CONNECTED COMPONNENTS LABELLING A connected component in a binary image is a set of pixels forming a connected group. For example, the binary image in Figure 6a has three connected components. Thus, connected component labeling is the process of identifying the connected components in an image and assigning each one a unique label, as shown in Figure 6b.

Otsu image thresholding

Erosion

Morphological filtering

Dilation

Connected components labelling Largest discharge detection

(a) (b) Figure 6. Connected components labelling on binary image.

.

4.4 CLASSIFICATION METHODS Three of the most commonly used classification methods are employed in this paper: the k-nearest neighbors’ classifier (Knn), the Naïve Bayesian classifier and Support Vector Machines (SVM). These three algorithms have been used and discussed in many domains [27-31]. The k-nearest neighbors (Knn) classifier [28] is simple and easy classifier. Knn requires no training and assigns an object to a class based on the classes of its k-nearest neighbors. Several distances can be used to determine the nearest neighbors. Knn is suitable for limited data set and can achieve better performance [28]. The Naïve Bayes classifier [29] is a simple probabilistic classifier based on Bayes theory. It implies the assumption that the variables are statistically independent and known to be rather effective. Support Vector Machines (SVM) [30, 31] is considered as one of the most accurate machine learning classifiers. This is because of its ability to solve problems of linear and non-linear classification by finding the maximum margin hyper plane to separate the classes. Linear, polynomial, RBF and sigmoid kernel functions can be used.

5 PROPOSED ALGORITHM This section is dedicated to explain different steps of the proposed algorithm for flashover monitoring. This algorithm aims to detect arcing discharges on a plane insulating surface. It contains four parts. First part, by using the optimal threshold value, Otsu segmentation extracts discharges appearing on the insulating surface. Second, morphological filtering is

Feature vector computation

Discharge classification Knn

Naïve Bayes

SVM

End

Figure 7. Flowchart of the proposed arcing discharge recognition algorithm .

5.1 SEGMENTATION AND FILTERING During experimental procedures described in section II, the applied voltage is varied, for several pollution conductivities, from 0 kVrms to the flashover voltage value (32 kVrms). Images are represented in Figure 8 for various applied voltage levels (15, 25, 30 kVrms). The first step consists to convert those images to binary ones through Otsu segmentation. This step is highly important. After the determination of the optimal threshold for a given image, discharge components are translated, on the binary segmented image, in pixels equal to 1. These components are represented by 0, while no apparent discharges are observed as shown in Figure 8b. Second step consists in morphological filtering. It is processed into two stages. The first stage corresponds to image erosion by eliminating pixels in superior insulator boundary region, as shown in Figure 8c. These pixels are clearly visible on the segmented image (Figure 8b) and do not represent any electrical discharges on the insulator surface. In fact, these white pixels, which are located in the top of images shown in Figure 8b, results from the reflection of laboratory lights on the insulator top edge. Following the erosion, dilation is implemented.

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25 kVrms

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30 kVrms

(a) Initial images

(b) Otsu segmentation

(c) Erosion

(d) Dilation

(e) Largest discharge detection Figure 8. Proposed image processing algorithm for flashover monitoring for various applied voltage levels

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A. K. Chaou et al.: Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition

Since erosion removes pixels from objects boundaries (Figure 8c), dilating the image aims to adds pixels to these regions (Figure 8d). That is to say, dilation is used after erosion to preserve the shape and size of larger objects (electrical discharges), while removing noises from the image (lights reflection). Note that the structuring element used in this work is a diamond shape. The distance between its origin and extremities is 3 pixels. This choice offers better recognition results.

image contains arcing discharges, even small ones, it is classified in arc discharge class. In order to perform a proper training and testing of classification methods, classes’ sets dimensions are chosen very close; from 747 images, 373 have been used for no arc discharge class and the rest for arc discharge class.

5.2 FEATURE EXTRACTION The third step is crucial. It is devoted to the calculation of the feature vector. This vector describes discharges properties and represents the input of classification methods. Labelling of connected components is processed on the filtered image (represented by Figure 8d). This labelling enables the detection of the largest connected component region. This same region symbolizes the largest discharge on the insulator surface and is contoured by a blue square in Figure 8e. Consequently, based on this contoured region, the feature vector is calculated. This vector includes four parameters described as follows:  : represents the number of connected labeled components on the image. That is to say, it corresponds to the number of electrical discharges on the insulator surface. Few discharges imply a small , while numerous discharges denote a large .  : stands for the number of pixels on the largest connected component region (contoured in blue square on Figure 8e). Practically, it represents the number of pixels on the largest electrical discharge area. Thus, is linked to the growth of the largest discharge. If is important, discharges occupy a large area on the insulating surface. Otherwise, if is small, it informs that discharges activity is localized in a small area.  : symbolizes the length of the largest labeled connected component, thus the length of the largest discharge area.  : corresponds to the width of the largest labeled connected component, thus the width of the largest discharge area. Since these four parameters quantify objects on an image, their unit is pixel.

This section is dedicated to expose different results obtained from the proposed image processing algorithm. Alike the previous section, this one is also divided in three parts.

5.3 DISCHARGE CLASSIFICATION The classification tools of an efficient pattern recognition algorithm able to monitor insulator flashover, have to be trained and tested over a large database. In our present work, as presented in Table 2, prominent image database is extracted from flashover process videos. 70% of this database is dedicated to the training process, while the rest is for testing. In order to choose the best classification tool which fits the proposed algorithm, Knn, Naïve Bayes and SVM methods are used and compared. Table 2. Image classification database.

Database

Images number

Training

521

Test

226

Total

747

For every image, a class is carefully attributed referring to Table 1. If the image shows only luminosities but no arcing discharges, it is classified as no arc discharge class. Else, if the

6 RESULTS

6.1 SEGMENTATION RESULTS The segmentation process was described in the previous section by the example represented in Figure 8. However, Figure 9 illustrates the necessity of morphological filtering before the feature extraction step. From Figure 9a, laboratory lights are clearly reflected on the upper insulator boundary. Such lights reflections are translated into white pixels on the Otsu segmented image (Figure 9b). This constitutes a misleading representation of discharges activity on the insulating surface. Hence, if no filtering is processed, largest discharge detection includes these undesired noises, as presented in Figure 9c. In this case, the largest discharge is falsely detected. Consequently, the four parameters based on this detection are incorrectly calculated. However, if morphological filtering previously cited is processed, the largest discharge is correctly contoured by a blue square, as showed in Figure 9d. Therefore, morphological filtering is necessary to eliminate noises on images before any further processing. 6.2 FEATURE VECTOR RESULTS Previously in Section 3, discharges leading to flashover were thoroughly studied on the polluted insulating surface under 1.2 mS/cm. Based on these images, Figure 10 portrays feature vector results at different applied voltage levels before the flashover. Results shown in this figure are obtained during two stages: absence of arcing discharges on the insulating surface, for applied voltage levels less than 20 kVrms, and presence of arcs for applied voltage levels higher than 20 kVrms.

(a)

(b)

(c) (d) Figure 9. Utility of morphological filtering. (a) Original image, (b) Otsu segmented image, (c) labeled image without filtering and (d) labeled image with filtering.

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First, , which represents the number of labeled connected components, shows much bigger values when arcing discharges are observed. This result is somehow predictable and proofs that discharges remain on the insulating surface with the approach of flashover. In general, increases with the increasing of the applied voltage. Presenting many fluctuations, this increase is not autonomous. This is due to the discharge intermittency phenomena corresponding to the transient behavior of discharges resulting from their very rapid appearance and disappearance. However, in case of arcing discharge, rising followed by a rapid falling, informs that arcs appear of and rapidly disappear.

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represents the number of pixels in the largest discharge area. It takes very low values (less than 1200 pixels) for weaker applied voltage levels (less than 11 kVrms). Such of suggest the absence of significant electrical values discharges. However, increases rapidly to reach a value of 7600 pixels for 20 kVrms. The increase of indicates that discharges are joined to occupy a large area on the insulating surface when no arc is noticed. However, in presence of arcs, presents low values (around 1700 pixels for 23 to 25 kVrms and 29 to 30.5 kVrms) accompanied by many pics reaching 6413, 4965, 4943 and 5696 pixels for 26, 27.5, 30.7 and 31.2 kVrms respectively. Low values point out that discharges are disjoined and separated, so no longer large discharge areas are present. In fact, small discharges tend to disappear in the neighborhood of arcs. Whereas strong confirms the rapid appearance and fluctuations of disappearing of arcs. represents the length of the longest discharge area. increases slowly. For applied voltage less than 11 kVrms, remains less than 45 pixels. Such values prove that discharges occupy a very small area. However, for applied voltage levels remains between 11 and 30 kVrms (before flashover), approximately constant and equal to 100 pixels accompanied

With the discharges elongation, increases up to its maximum value (36 pixels) for 31 kVrms. However, decreases just before flashover to its minimum value (5 pixels). represents an important result and The decreasing of indicates that the number of discharges decreases. Such result represents an important indicator to predict the flashover and is due to the formation of the principal arc. This arc rises to form the disruptive arc, if the applied voltage is slightly increased. The growth of such discharge implies the extinction of other small discharges on the insulating surface. No arc discharges

Arc discharges

40

32

24

16

8

0

14000 12000 9000 6000 3000 0 500

400

300

200

100

0 400

300

200

100

0

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Applied voltage (kVrms)

Figure 10. Feature vector indicators during the flashover process for a conductivity of 1.2 mS/cm.

28

30

32

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A. K. Chaou et al.: Elaboration of Novel Image Processing Algorithm for Arcing Discharges Recognition

by very small fluctuations. Between 30 kVrms and the flashover voltage value (32 kVrms), we note a fast increase of up to 484 pixels. This fast increase of indicates that the final discharge rises and elongates very rapidly leading to flashover. The presence of localized discharges tends to create a dry band. Indeed, can be seen as the length of the largest dry band on the insulating surface. indicates the width of the largest discharge area and increases very slowly to reach its maximum value (115 pixels) for 20 kVrms. This variation points out that discharges expand and get wider in absence of arcs. However, in presence of arcs, decreases from 110 pixels (obtained between 20 kVrms and 22.5 kVrms) to less than 40 pixels (found between 23 and 31 kVrms). This decreasing indicates that arcs get less wide while elongating. Analogously to , can be regarded as the width of the largest dry band. Hence, low values of are justified by the fact that arcs short-circuit only a part of the dry band length, without bridging all band dry width. Consequently, arcs width is always shorter than the dry band one. Before the flashover, increases rapidly. This indicates that the final arc becomes wider to form the flashover discharge. From the results exposed in this section, we can affirm that the four feature vector indicators have a specific behavior when arcing discharges are observed on the insulating surface. Hence, these indicators can be used as inputs for supervised learning methods in order to detect the presence of arcing discharges. 6.3 CLASSIFICATION RESULTS The four indicators are computed for every image of the database (Table 2) and represent the inputs of classification methods. Referring to Table 1, the output is either presence or absence of arcing discharge on the insulating surface (bi-class problem). In this work, simplest tuning of classification algorithms is chosen. For SVM, a linear kernel is selected. While for Knn, the Euclidian distance is chosen, with a k value equal to 1. Recognition rate, which represents the percentage of successfully classified images over the total test database, is presented in Table 3. Table 3. Discharge classification results.

Recognition Rate (%)

Knn

Bayes

SVM

92.47

88.05

95.57

From Table 3, results announce SVM as most suitable choice for arcing discharges recognition. This is due to the ability of SVM to deal with linear and nonlinear problems. In this case, inputs of classification methods are clearly showing high nonlinearities and complex correlation with the output class. Hence, SVM is recommended. On the other hand, Knn offers rather good results despite its simple classification rule. Results obtained by Knn might be ameliorated by optimizing the value of k. Finally, Naïve Bayes algorithm gives the worst results, demonstrating thus the inadequateness of such algorithm in this work. This might be due to its independent feature assumption.

7 CONCLUSION The work described in this paper introduces a novel methodology to analyze discharges activity and monitor flashover. Experiments were established on a plane insulating surface for better visibility of appearing discharges. Through a proposed algorithm, four indicators are selected to characterize discharges activity and to discern between arcing and not arcing discharges on the insulating surface. Further, pattern classification (Knn, Naïve Bayes and SVM) is processed to automate arcing discharges recognition. Experimental observations show that flashover process develops over 7 stages. These stages have been classified into two classes. The first one is characterized by the presence of arcing discharges and the second one by their absence. Hence, these two states can be used to monitor flashover. Otsu segmentation method gives promising results when it is applied on images extracted from the flashover process videos. This segmentation method characterizes very well texture of discharges on the insulating surface. The use of morphological filtering, through erosion then dilation, results in eliminating noises on images before any further processing. If this crucial step is ignored, discharges classification will be erroneous. The four proposed indicators computed based on the filtered images, allow to quantify very interesting phenomena: and during the flashover process - Fluctuations of allow to put forward discharge intermittency. This intermittency phenomenon is more accentuated during final stages of flashover process. This phenomenon corresponds to appearance followed by a rapid disappearing of discharges on the insulating surface. - With the applied voltage increase, the number of discharges on the insulating surface, quantified by , increases. However, it decreases rapidly just before flashover (1 kVrms before flashover voltage value). Such decrease indicates the extinction of small discharges before disruptive discharge rising. - Our paper proposes a novel processing method to analyze and investigate the appearance and disappearing of arcs consisting in the fluctuations of . and allow to quantify surface occupied by these arcs. In early stages of the flashover process, discharges are joined on wide areas; and are important. Extended presence of these discharges tend to form dry bands. Then, arcs are formed in these bands engendering the diminution of . Best classification method has to be properly selected. Depending on the input data, classification results are very different. In this work, SVM shows a superior performance comparing with Knn and Bayes classification, with a recognition rate equal to 95.57 %. The obtained classification results proof that the four proposed indicators are trustworthy to detect arcing discharge on the insulating surface. The obtained recognition rate confirms efficiency of the proposed algorithm. This study can be extrapolated to investigate discharges on real outdoor insulators.

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Ahmed Khaled Chaou received the degree of Engineer and Masters degree in High Voltage Engineering from Ecole Nationale Polytechnique (ENP) of Algiers in 2012. He is currently a Ph.D. degree student at the Electrical Engineering Department in the same School. His research interests are in artificial intelligence, signal processing and pattern recognition for polluted insulators diagnosis.

Abdelouahab Mekhaldi was born in Algiers, Algeria in 1958. He received the degree of Engineer in 1984 in electrical engineering, a Masters degree in 1990 and a Ph.D. degree in high voltage engineering in 1999 from Ecole Nationale Polytechnique (ENP) of Algiers. He is currently a Professor at ENP of Algiers, His main research areas are in discharge phenomena, outdoor insulators pollution, polymeric cables insulation, lightning, artificial intelligence application in high voltage insulation diagnosis and electric field calculation. Madjid Teguar obtained a first degree in electrical engineering in 1990, a Masters degree in 1993 and a Ph.D. degree in high voltage engineering in 2003 from Ecole Nationale Polytechnique (ENP) of Algiers. He is now a Professor in electrical engineering at ENP. His research interests are in insulation systems, insulation coordination, earthing of electrical energy systems and polymeric cables insulation