Fault Detection on Transmission Lines Using a Microphone Array and an Infrared Thermal Imaging Camera

Ha, Hyunuk; Han, Sun-Sin; Lee, Jang-Myung

doi:10.1109/tim.2011.2159322

Cited by 74 publications

(20 citation statements)

References 22 publications

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“…A sensitive insulator life detection method is proposed in [7]. Sound wave and the ultrasonic wave is applied on faulty insulators detection of high-voltage transmission line in [8][9][10][11]. In recent years, aerial photos and image processing technology are also involved in failure insulator online detection [12].…”

Section: Introductionmentioning

confidence: 99%

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

Wang

et al. 2020

IEEE Access

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A contactless method based on infrared image matching is presented for zero-value insulator detection on outdoor porcelain insulator string. An improved SIFT (scale-invariant feature transformation) method is developed to extract features and accomplish the pre-matching between the insulator string to be detected and the standard string in the image library. An improved RANSAC (random sampling consistency) algorithm is developed to remove mismatching points and achieve more accurate and faster detection. Firstly, an adaptive circle window is designed to improve the sensitivity of the SIFT operator on arc features extraction from insulators. Then, the dimension of the feature is decreased from128 to 32 by dividing the circle into 4 fan-shaped regions and descript the feature on 8 directions to achieve the lower computation burden and high accuracy. For the SIFT method and RANSAC method uses Euclidean distance to measure the similarity between features extracted from the string to be detected and the string in the standard library, mismatching may be caused. Spatial geometric features of the neighborhood around the feature point are used to construct the error function to improve the RANSAC method and remove the mismatching points. Faster matching is obtained by a threshold control for decreasing the number of data checking for the consensus set data models. Testing results show that the presented method is effective in the detection of zero-value resistances for outdoor insulator string. Compare with the SIFT method and SIFT+RANSAC method, the presented method has higher accuracy and faster detection speed.INDEX TERMS Zero-value insulator detection, infrared imaging, image matching, adaptive circle template, spatial geometric features, prosumer infrastructure

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Section: Introductionmentioning

confidence: 99%

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

Wang

et al. 2020

IEEE Access

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“…In these dangerous environments, a nondestructive inspection approach is useful for mitigating concerns regarding potential accidents [1]. These methods measure corona discharge [5], ultrasonic emissions [6], and heat generation [7] from a damaged system. With the technological advances in the fourth industrial revolution, these methods have attracted attention.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection With an Ultrasound Array and Deep Convolutional Neural Network Fusion

Kim

Jeong

et al. 2020

IEEE Access

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Diagnostic methods for power transmission facilities are important for energy security because the growth of defects in power facilities increases the risk of blackouts in an entire power grid. However, damage in power transmission facilities is difficult to detect because cracks or defects are minuscule and are challenging to determine. One interesting phenomenon caused by damage in power transmission facilities is ultrasound emissions on a damaged surface. However, measuring ultrasound emissions to detect defects is limited by the severity of the surrounding noise. To overcome this limitation, this study proposes a new method for damage detection by fusing ultrasound measurements with recorded optical images. The proposed method consists of two phases. The first phase preprocesses ultrasound measurements for ultrasound feature extraction. This phase aims to detect the location of ultrasound emissions by analyzing ultrasound characteristics including the intensity and density. The second phase detects and classifies a damaged object with optical images recorded using a deep convolutional neural network. This phase not only discards the noise from the ultrasound measurements but also classifies a damaged system among many components in power transmission facilities. The experiments validate the effectiveness of the proposed method using ultrasound measurements and recorded images and finally suggest scenarios for potential applications.

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“…Commonly used contact inspection methods in the field include the HI-Pot test, partial discharge measurement method, electric field measurement method, and insulation resistance measurement method [7][8][9][10]. In addition, recently, non-contact infrared scanning and image analysis methods for measuring mechanical damage from a long distance have been studied [11,12]. However, for the above measurement method, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

et al. 2020

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The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.

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Fault Detection on Transmission Lines Using a Microphone Array and an Infrared Thermal Imaging Camera

Cited by 74 publications

References 22 publications

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

Damage Detection With an Ultrasound Array and Deep Convolutional Neural Network Fusion

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

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