A Detection Method for Transmission Line Insulators Based on an Improved FCM Algorithm

Wang, Bowen; Gu, Quan

doi:10.12928/telkomnika.v13i1.1319

Cited by 10 publications

(10 citation statements)

References 15 publications

(7 reference statements)

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“…From the bounding boxes, features like mean, standard deviation were extracted and successfully tested for automatic insulator extraction from the plain background and complex background [1] as well. Fuzzy C-Means algorithm (FCM) as proposed by Bo Wen Wang, Quan Gu [39] is used to recognize transmission line insulators. To filtrate and recover image in pre-processing, the improved Wiener filter algorithm was used and then, improved FCM was used to segment the insulator.…”

Section: Spatial Domain Methodsmentioning

confidence: 99%

“…Among the clustering methods used to segment the object of interest, the K-Means algorithm involves more error cluster pixels, whereas FCM algorithm involves less. The algorithm using FCM [39] have a good segmentation effect, effectively reducing the number of error cluster pixels. Local binary pattern (LBP) is a type of spatial domain based feature used widely for classification in the field of computer vision.…”

Section: Spatial Domain Methodsmentioning

confidence: 99%

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Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Prasad¹,

Rao²,

Ece³

2016

JESTR

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In the present world, there is a great necessity to have reliable and quality power distribution, and so there is great scope for research on automation of distribution system. The main objective of this paper is to analyze and comprehend different machine learning and image processing based algorithms to find a practical solution for automated inspection of overhead power line insulators. This method is a relatively new approach for. This paper also highlights the constraints and limitations that are present in the various existing methodologies to achieve the objective. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. The few techniques like artificial neural networks (ANN), Hidden Markov Model (HMM), k-means clustering, Wavelet transform features, S-transform features, and support vector machines (SVM) applied in the domain of condition monitoring of the insulators were presented.

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Section: Spatial Domain Methodsmentioning

confidence: 99%

Section: Spatial Domain Methodsmentioning

confidence: 99%

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Prasad¹,

Rao²,

Ece³

2016

JESTR

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“…To simplify the task of acquiring the images containing only insulators, image segmentation has been successfully done using proven techniques such as Canny edge detection, Hough transform in conjunction with SVM [37]. In each bounding box, the insulator's presence was detected by extracting some features like color features [33] and extracted features from the bounding boxes were supplied to an SVM classifier and presence of insulator was detected.…”

Section: Figure 1 Insulator Monitoring Systemmentioning

confidence: 99%

Review of Machine Vision Based Insulator Inspection Systems for Overhead Power Distribution System

Prasad

Rao

2017

IJAAS

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The necessity to have reliable and quality power distribution is increasing, and hence there is great scope for research on automation of distribution system. There are signs of increased research in the work on condition monitoring of insulators during the last few decades. The possible failures can be predicted before they actually occur by using the condition monitoring of cables or any electrical equipment on-line. Those assets such as towers, conductors and insulators which are on the threshold of failure have to be replaced or repaired, so that forced outages reduce. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. In developed countries aerial patrolling has been adopted to monitor the insulators as an alternative. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. This review paper covers overall aspects of automatic detection of defects of insulator systems of electric power lines and classification into different classes by using vision-based techniques.

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“…Images of transmission lines can also be acquired by using cameras or video cassette recorders (VCRs) carried on an UAV. After obtaining these aerial photographs, many methods have been proposed to detect and locate the insulators in the images with the difficulty of complex background and low image resolution [14][15][16][17][18][19][20]. In [14], an improved segmentation Fuzzy C-Means algorithm (FCM) is proposed to accurately segment insulators from the image.…”

Section: Introductionmentioning

confidence: 99%

“…After obtaining these aerial photographs, many methods have been proposed to detect and locate the insulators in the images with the difficulty of complex background and low image resolution [14][15][16][17][18][19][20]. In [14], an improved segmentation Fuzzy C-Means algorithm (FCM) is proposed to accurately segment insulators from the image. In [15], a method based on the Histogram Oriented Gradient which could precisely extract the insulators from the images and is suitable for many practical applications such as insulator fault diagnosis, insulator contamination grade determination and so on was described.…”

Section: Introductionmentioning

confidence: 99%

An Online Measurement Method for Insulator Creepage Distance on Transmission Lines

et al. 2018

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Abstract:Insulators play a crucial role in ensuring the normal operation of the power system, and the creepage distance is an important electrical parameter of insulators. Most available solutions focus mainly on offline measurement methods, and online measurement for insulator creepage distance on transmission lines remains a challenging task. To address this issue and to further improve the corresponding work efficiency, an online measurement method for insulator creepage distance is presented in this paper. Considering the glass material of the insulator and the long measuring distance, this method recognizes the insulator type indirectly by calculating the structural parameters of the insulators based on their geometric features, and then obtaining the creepage distance. Accordingly, a measurement system, which mainly includes an electronic total station and a camera with a telephoto lens, is designed in this paper. Moreover, this paper also proposes an error analysis model aimed at reducing the errors caused by the layout of this system. In the conducted experiments, this proposed method effectively obtains the creepage distance and the error correction model can further improve the measurement accuracy of structural parameters.

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A Detection Method for Transmission Line Insulators Based on an Improved FCM Algorithm

Abstract: An improved segmentation Fuzzy C-Means algorithm (FCM)

Cited by 10 publications

References 15 publications

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Review of Machine Vision Based Insulator Inspection Systems for Overhead Power Distribution System

An Online Measurement Method for Insulator Creepage Distance on Transmission Lines

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