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1.IntroductionGlass insulators are widely used in power systems due to their high resistance, stable electrical strength, slow aging, etc. But the self-shattering, cracking, and serious contamination of insulators often appear under complex and harsh operating conditions, which will lead to serious accidents without timely detection and replacement. Thus, how to timely detect the insulator defects is an important task for the safe operation of transmission lines. Insulator defects have been highly concerning in transmission lines and the related detection technologies have been developed consequently. Traditionally, insulator defects were inspected by the human patrol every one or more months, which is inefficient, subjective, and dangerous for the patroller. With the development of economy and technology, the detection technologies are becoming more and more intelligent. Now, machine visual and image processing have been developed in recent years with the computer vision techniques. More video monitoring devices (VMDs) have been installed on towers in China1 or unmanned aerial vehicles (UAVs) with cameras started patrolling transmission lines,2–4 which are used to capture insulator images and monitor the running status of transmission lines, then the obtained images are often analyzed automatically using a customized image processing algorithm. For example, contamination detection, hydrophobic detection, and crack detection have been gradually applied in practice. Machine visual and image processing have the advantages of high speed, low cost, and high performance, which are regarded as the most attractive techniques for defect detection. This paper mainly focuses on self-shattered glass insulator, which refers to the phenomenon that the insulator self-shatters and falls off under the harsh environment. Once the glass insulator self-shatters, it will have great harm to the entire transmission lines. How to timely detect and replace the self-shattered insulator is an urgent and necessary task. The detection technology of glass insulator defects mainly consists of two parts, which are the insulator identification and the defects’ localization. In terms of the insulator identification, many researchers have studied how to extract insulator by image processing, which mainly relies on the inner characteristics of the insulator such as color, shape, and texture. For example, the clear insulator strings are obtained by threshold segmentation and morphological corrosion of I-space images in hue, saturation, intensity (HSI) space.5 An improved algorithm is proposed to extract the S-space image in the HSI space and the maximum interclass variance threshold segmentation is performed for the extracted image.6 The maximum entropy threshold segmentation method is used to extract the insulators and the Hough linear detection and genetic algorithm are proposed to remove the interference.7 However, there are few studies that identify the defects of insulators. For example, the edge shape of the insulator is used to judge whether the insulator exists the self-shattered defect, but a complete edge is needed in the chain-code analysis.8 The self-shattered defect is judged by calculating the number of ellipses and the distance between the ellipse centers.9 But the aforementioned edge shape is obtained based on the insulator features, which are very difficult to obtain a complete edge under the complex background.8,9 The texture features of the insulator are extracted and the defects are judged by the analysis of the texture features, but the algorithm does not propose an effective segmentation scheme.10 Based on the analysis aforementioned, these algorithms can be only used for the ideal clear images. But it is difficult to capture the clear images by the VMD or UAV. Furthermore, these images are usually characterized by vague, color difference, deformation, and partial occlusion due to the dark environment, the varied topology, the shot angle, the camera resolution, and the contamination level. It is difficult to accurately recognize the defects of insulator strings, by relying only on certain insulator features, such as color, shape, and spatial sequence. In practice, we find that the insulator texture feature is relatively stable for most captured insulator images. Thus, an automatic identification and location technology of self-shattered glass insulator is presented, by which the self-shattered insulators can be accurately identified and located by extracting its local binary pattern (LBP) feature.11–13 2.Framework of Automatic Identification and Localization TechnologyAn automatic identification and localization technology of self-shattered glass insulators consists of three parts: cameras, which can be installed on towers or UAV, the 4G/OPGW communication network, and the monitoring center, where the identification and localization algorithm is embedded into the expert software, as shown in Fig. 1. The images of the glass insulator string are first captured, decompressed, and sent to the monitoring center by 4G/OPGW. The self-shattered insulators can be identified and located from the images in the monitoring center based on the identification and localization algorithm of self-shattered glass insulators built. 3.Algorithm for Identification and Localization Technology of Self-Shattered Glass InsulatorsIn most cases, a single insulator string or double insulator strings can be considered to be distributed as one or double straight lines. The contour of a single insulator is similar to an elliptical shape, and the number of insulators is mainly related to the running voltage of transmission lines. Thus, an insulator string in a given image [see Fig. 2(a)] can be considered as a row of approximate ellipses, whose length is the distance between the starting point and the end point along the row. Then, a linear-fitting straight line can be obtained to estimate the direction and length by fitting the center coordinate of each insulator by the least squares method. Figure 2(b) shows the proposed mathematical model of the insulator string. We set the lower left coordinate of the image as the origin and the center of each insulator contour as (). When , it stands for the first insulator and is the last insulator. The mathematical model of the insulator string can be obtained by a linear fitting step, which can be summarized as follows:
Then, the partial derivations of and in Eq. (2) are calculated and simplified. The optimal and can be obtained by Eq. (3) and the straight-line equation by Eq. (1) Then, the angle of the line can be obtained as The length of insulator can be obtained as The distance of two adjacent insulators is defined as and the average distance between two adjacent insulators can be obtained as 4.Identification and Localization of Self-Shattered Glass InsulatorsAlthough the method of image processing to identify insulator defects has already been studied by some scholars, there are two difficulties related to this project.
The available defect recognition algorithms often rely on color or shape of insulators, which is ineffective and unstable due to the limitations of insulator segmentation and unstable features. An automatic identification and location algorithm of self-shattered glass insulator is presented, which consists of identification of glass insulator string and detection of self-shattered insulators. 4.1.Identification of Glass Insulator StringA captured insulator image commonly consists of the insulator string and other background objects, such as tree, tower, conductor, hill, grass, and mountain. Especially, when the cameras work in the high voltage circumstance, the image quality is greatly influenced due to the electromagnetic interference. In addition to the complex background aforementioned, there are abundant noises in the insulator images. To accurately extract the insulator, the insulator region should be first identified and extracted. The HSI color space, which consists of image color space conversion and image segmentation, is adopted in this paper.
Table 1Dimensional information of the insulator string.
Next, the distance of adjacent insulators can be calculated by both center points of adjacent insulator contours, as shown in Fig. 2. But the distances of adjacent insulators in the insulator strings may vary in a certain range, to ensure the accuracy of the calculation, the average distance of adjacent insulators is calculated with the maximum and the minimum distances eliminated. The calculated average distance is also close to the actual distance of adjacent insulators, which is beneficial to calculate the length of blocks and the size of template. Table 1 illustrates the dimensional information of the insulator string such as the center point of every insulator, the distance of adjacent insulators, and the calculated average distance of adjacent insulators for the whole string. 4.2.Detection of Self-Shattered InsulatorsBy matching the LBP histogram of blocks with that of sliding template, the self-shattered insulator can be recognized. The procedure consists of LBP histogram extraction based on blocks, determination of sliding blocks, and location of self-shattered insulators. 4.2.1.Local binary pattern histogram extraction based on blocksSome image features such as color, shape, texture, and spatial sequence are commonly used to extract the insulators from images.17,18 It is found that the insulator texture feature is relatively stable, in a sense, the gray values of texture are also displayed regularly, which exists a certain gray relationship between two pixels in the image space. When the insulators self-shatter, the surface texture will be obviously changed. There are many well-established approaches to extract the texture feature, such as gray-level co-occurrence matrix (GLCM),19 gray differential statistics, and wavelets analysis.20 These methods have a certain effect in the texture analysis, while they are rarely used in actual applications. In recent years, the LBP method was proposed by Ojala et al.,21 which has a low-computational complexity, a multiscale feature, and a rotational invariable feature, and is widely used in the field of texture classification and face recognition. Then, the LBP method is proposed to identify the insulator string, which consists of three steps as follows: calculating the binary relationship of each pixel and its neighboring pixels in the image, forming the local binary model by weighted rules for the relationship, and getting binary image pattern by the multiregion histogram sequence.
4.2.2.Determination of sliding blocksFor transmission lines, the function of the insulator string is to connect the tower and conductor. For the tension tower, the insulator string is nearly vertical. But for the suspension tower, the direction of the insulator string is at any angle. If the whole insulator can be analyzed timely, the sliding direction and the length of blocks are very important.
Then, the length of normal blocks is and the length of the last smaller block is defined as But for double insulator strings, the obtained region of insulator strings needs to be divided into two subsidiary parallelograms, which contain each row of insulators, which is two times of the number of adaptive blocks of single insulator string. The information of adaptive blocks for six test images is shown in Fig. 6. 4.2.3.Localization of self-shattered insulators
First, the average distance is obtained by Eq. (14). If , the matched block is not self-shattered block Second, the difference between and is calculated by Eq. (15) which is normalized as by Eq. (16) Finally, based on the normalized , every block can be recognized if it is a self-shattered block by threshold according to Eq. (17), then the relative displacement between the self-shattered block and start point is obtained, hence the position of self-shattered insulators is located 4.3.Application of the Identification and Localization Algorithm of Self-Shattered Glass InsulatorsFigure 8 shows the whole process of the image processing algorithms aforementioned. The self-shattered insulator image is captured [see Fig. 8(a)], which has a complex background and great noise. The conversion of the color space and image segmentation is used to segment images [see Fig. 8(b)]. The idea of “and” operation is used to get the intersection of the segmentation of and components to obtain relative pure insulator strings as much as possible [see Fig. 8(c)]. Then, the morphological processing and contour detection are used to obtain the connected domains of single insulators [see Fig. 8(d)] and extracts the area and center. Based on the analysis of the previously mentioned processes, we establish the mathematical model of insulator string. Next, the LBP histograms based on adaptive block are extracted [see Figs. 8(e) and 8(f)], then the template and direction of sliding window are established [see Fig. 8(g)]. Finally, the LBP histogram of the regions between the sliding window and the template is calculated and analyzed to recognize and locate the self-shattered defects [see Fig. 8(h)], the left picture is the histogram of the template and the right is the histogram of a block. The recognition result is marked with a red rectangle [see Fig. 8(i)]. 5.Experimental Results and AnalysisThe performance of the proposed LBP histograms is analyzed in this section, which will be compared with the related well-established methods. All images used in this experiment are obtained from different insulator strings and different view angles under different conditions, which are captured by VMDs of transmission lines and whose size is . A dataset with 400 images is constructed to evaluate the performance, 200 images of which have one or more defects and 200 images no defect. 5.1.Comparison of Local Binary Pattern Histograms with Other Two Similar MethodsTwo common texture feature extraction methods are selected to compare with the texture feature extraction based on the LBP histogram. Method 1 is a feature extraction method based on the gray-level histogram;19 the color image is first converted to the gray image and count the gray image histogram, and then calculate the mean gray and calculate the dispersion, variance, skewness, kurtosis of gray histogram whose four feature vectors describe the image texture characteristics. Method 2 is a feature extraction method based on the GLCM.19 Because the calculation for the co-occurrence matrix is large, the gray scale is coarsely quantified from 256 levels into 16 levels, which can save computing time. Although the distortion exists after the image is quantified, it has little effect on the texture feature. Then, the GLCM of the four directions (0 deg, 45 deg, 90 deg, 135 deg) is calculated, generally, four commonly used characteristics are the contrast, entropy, energy, and correlation. Finally, the average value is calculated to describe the texture characteristic of image. We extract the texture with a group of insulator images, which are the same in Fig. 6, and calculate the similarity distance between the feature vector of template insulator block and each block in the insulator image, but if the similarity distance is directly calculated, the difference of the value will affect the weights of feature vectors. Therefore, the feature vector is first normalized, and the normalized equation is defined as where represents the original feature vector [], represents the normalized feature vector [], and represent the mean and standard deviation of the original feature vector, respectively.The Euclidean distance is adopted to calculate the similarity distance. We assume that [] represents the feature vector of the template block in every insulator image, [] represents the feature vector of each block in the insulator image, the smaller similarity distance is, the higher the similarity between the template block and each block is. The range of normalized similarity distance is 0 to 1 and the similarity distance is defined as Because the number of blocks in every image is different, the Euclidean distance between the template and the blocks can be calculated by these methods. As seen from Table 2, for most test images, the calculated distance based on LBP histogram is much smaller than that based on methods 1 and 2, which means that the proposed method has highest matching accuracy. Method 1 is the easiest and simplest to extract the texture feature, but its matching accuracy is the worst because it only relies on the gray histogram and lacks of the pixel space information. Method 2 is especially complex with co-occurrence matrix, but its matching accuracy is higher than the proposed method only in the No. 2 image. The proposed LBP histogram is very effective on the feature extraction of the insulators. Table 2Comparison of the Euclidean distance with other two methods.
5.2.Identification Examples of Self-Shattered InsulatorsBy matching the LBP histogram of blocks with that of sliding template, the self-shattered insulator can be recognized effectively. When LBP histogram distance at some insulator is much bigger than others, this insulator maybe a self-shattered insulator. There are two examples used to illustrate the identification of self-shattered insulators. In addition, all the self-shattered insulators are labeled with a minimum red rectangle, by which it is easy to find and locate the self-shattered insulator.
5.3.Identification Performance of Self-Shattered Insulators
Figure 10 shows the testing results of some typical images and Table 3 shows the recognition effect of the algorithm. As seen from Table 3, for single insulator images, , , and of the presented algorithm are 94.5%, 92.38%, and 96.78%, respectively. For double insulator images, , , and are 90.00%, 86.36%, and 93.23%, respectively. All running results show that the algorithm is effective and practical. Table 3The recognition effect of the algorithm.
5.4.Comparison of Three Detection MethodsThe proposed algorithm based on LBP histogram is characteristic of the texture feature. But the detection methods based on the edge or the shape are commonly used, by which the edge and the shape of normal region and abnormal region are fitted. We adopt three different detection methods to identify and locate the self-shattered insulators. The experiments are carried on a laptop with an Intel(R) Core(TM) i5 CPU (2.67 GHz) and 4-GB memory. Three hundred images are selected to be analyzed. The mean running times from extracting insulator region to locating the defects are recorded. The number of successfully detecting the self-shattered insulator in 300 images is also recorded. The results are shown in Table 4. Table 4Comparison of this method with other related methods.
As seen from Table 4, the accuracy of the proposed method is up to 90.63%, which is much higher than that of other two methods. Because of the instability of the shape, the methods based on shape or edge shape are prone to change easily with the different shooting angles and the varied illumination. The proposed method based on texture is relatively stable, which is suitable for detecting the self-shattered glass insulator. But the mean running time of proposed method is bigger than other two methods. For transmission lines, the running time difference in the second level does not matter. 6.Field Running Tests6.1.Field InstallationAn automatic identification and localization technology, as shown in Fig. 1, is successfully developed, which was already applied to many 110 kV or more transmission lines belonging to the Guizhou power grid in China. The UAV with the high-definition camera installed was also applied to the Shaanxi power grid, as shown in Fig. 11. 6.2.Identification of the Self-Shattered Insulators by Expert SoftwareIn this paper, the insulator recognition and localization algorithm is designed and embedded into the expert software installed in the monitoring center. The captured images are first compressed and then transferred to the monitoring center by 4G/OPGW. The images will be analyzed by the expert software, and then the identification results will be displayed in a timely fashion and sent to the staff by GSM SMS. The expert software is developed combining VC++ and HALCON, whose core functions consist of the image reading, insulator recognition, and defect localization, etc. The expert software also has other functions such as file operation, display, algorithm and analysis, defect information display, as shown in Fig. 12. Figure 12(a) shows the recognition of the insulator string and Fig. 12(b) shows the detection and localization of the self-shattered insulator. 7.ConclusionAn automatic identification and localization technology of self-shattered glass insulators is proposed, by which the self-shattered insulator can be identified and replaced in a timely manner. The proposed algorithm based on LBP histogram is characteristic of the texture feature, whose accuracy is much higher than that of two methods based on shape or edge shape. The technology has been applied to many 110 kV or more transmission lines, which is effective and reliable. However, we only aim at the kinds of images captured from natural illumination and complex background. In the case of images captured in fog, rain, and other complex climate, the method yields a low recognition rate. Meanwhile, compared with the actual world, the set coordinates still show certain differences. These situations deserve further research and improvement. AcknowledgmentsThe authors thank the referees for many valuable comments given to help improve this paper. This paper was supported by the Project of Key Science and Technology Innovation Team of Shaanxi with the Grant No. 2014XT-07 and the Shaanxi Industrial Science and Technology tackling key problems fund with the Grant No. 2016GY-052. ReferencesA. Pritchard and J. T. Vigil,
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BiographyXinbo Huang received his BS and MS degrees in automation from Qingdao Technological University, Qingdao, China, in 1998 and 2001, respectively. He received his PhD in automation from Xidian University, Xi’an, China, in 2005. Currently, he is a professor at the School of Electronics Information, Xi’an Polytechnic University, and also a PhD supervisor at the School of Electro-Mechanical Engineering, Xidian University. His current research interests include online monitoring technology, image recognition technology, and the wireless network sensor. Huiying Zhang received her BS degree in electronic information engineering from Xi’an Polytechnic University, Xi’an, China, in 2015. Currently, she is a graduate student, majoring in signal and information processing. Her research interest includes fault detection based on image processing. Ye Zhang received her BS and MS degrees from Xi’an Polytechnic University, Xi’an, China, in 2011 and 2014, respectively. Currently, she is a PhD student in mechatronic engineering from Xidian University. Her main research interests have been focused on intelligent power and online monitoring technology. |