Images Detection and Diagnosis of Corona Discharge on Porcelain Insulators at 22.9kV D/L

Shong, Kil-Mok; Kim, Young-Seok; Kim, Sun-Gu

doi:10.1109/demped.2007.4393138

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…Due to various factors, such as complex outdoor environment, improper maintenance and debugging, etc., the faults and defects of disconnector frequently occur [1]. At present, the mature technologies, including infrared temperature measurement technology [2]- [4], ultraviolet imaging detection method [5], pulse infrared wave thermal technology [6], contact resistance measurement, etc., are mainly focused on the overheating or insulator damage defect. For the detection and diagnosis of mechanical defects of disconnectors, there are few published research reports.…”

Section: Introductionmentioning

confidence: 99%

Defect Diagnosis of Disconnector Based on Wireless Communication and Support Vector Machine

et al. 2020

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Focusing on the shortage of mechanical defect detection and diagnosis technology for disconnectors, a wireless monitoring method for the mechanical state of disconnectors is proposed. The splitcore current sensors and improved voltage sensors are used to measure the motor currents and voltages of the disconnector under typical mechanical states at different working voltages. The wireless communication network is used to upload the acquisition data to the cloud server quickly, and the received data are processed by the software system. By comparing and analyzing the curves of current, input power, and output power under different states, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector. Twenty-three time-domain features of the output power time curve are extracted to form the original feature vector. Kernel principal component analysis (KPCA) method is used to reduce the dimension of the nonlinear features, and the Fisher's criterion function is constructed to determine the width parameter of the kernel function in the feature optimization. Grid search algorithm is used to optimize the kernel parameters of the support vector machine (SVM), and the trained SVM model is used to classify the mechanical state data whose working voltage part is known, and part is unknown, with a classification accuracy of 100%. The results show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector and has a good generalization ability.

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Section: Introductionmentioning

confidence: 99%

Defect Diagnosis of Disconnector Based on Wireless Communication and Support Vector Machine

et al. 2020

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“…Recent studies and advancements in the field of image processing and computer vision, especially the development of Convolutional Neural Networks (CNN) have enabled improving the performance of image analysis at the level of near human intelligence. On the other hand, with the rapidly expanding scale of power grids, traditional methods of manual inspection using electromagnetic field meters is far from satisfying the growing demand, due to the vulnerability to bad weather conditions and high prices [ 4 , 5 , 6 ]. Consequently, in recent years, many researchers have developed image processing techniques to detect electrical facilities including power lines [ 7 , 8 , 9 , 10 ], electric poles [ 11 , 12 , 13 ] and insulators [ 14 , 15 , 16 , 17 ] and analyze their defects [ 18 ] using aerial or ground images.…”

Section: Introductionmentioning

confidence: 99%

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

Siddiqui

Park

Lee

et al. 2018

Sensors

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Electric power line equipment such as insulators, cut-out-switches, and lightning-arresters play important roles in ensuring a safe and uninterrupted power supply. Unfortunately, their continuous exposure to rugged environmental conditions may cause physical or electrical defects in them which may lead to the failure to the electrical system. In this paper, we present an automatic real-time electrical equipment detection and defect analysis system. Unlike previous handcrafted feature-based approaches, the proposed system utilizes a Convolutional Neural Network (CNN)-based equipment detection framework, making it possible to detect 17 different types of powerline insulators in a highly cluttered environment. We also propose a novel rotation normalization and ellipse detection method that play vital roles in the defect analysis process. Finally, we present a novel defect analyzer that is capable of detecting gunshot defects occurring in electrical equipment. The proposed system uses two cameras; a low-resolution camera that detects insulators from long-shot images, and a high-resolution camera which captures close-shot images of the equipment at high-resolution that helps for effective defect analysis. We demonstrate the performances of the proposed real-time equipment detection with up to 93% recall with 92% precision, and defect analysis system with up to 98% accuracy, on a large evaluation dataset. Experimental results show that the proposed system achieves state-of-the-art performance in automatic powerline equipment inspection.

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“…However, in practical applications, this parameter still has some deficiencies because of its complex relations with observation angle, imager gain, observation distance, and imager types. To overcome these problem, several scholars have attempted to quantify discharges by UV image processing [18][19][20]. However, more unified conclusions are required.…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Factors on Electrical Equipment UV Corona Discharge Detection

Jiang

et al. 2016

Energies

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Abstract:As a non-contact discharge detection method, ultraviolet (UV) imaging can rapidly, directly, and securely detect corona discharges. Therefore, UV imaging has been widely applied to power systems. To study the influences of different factors on UV corona discharge detection, two typical types of UV imagers (DayCor ® Superb and CoroCAM ® 6D) were utilized. Results show that the observation angle has little impact on UV detection if no obstacles block the detection line of sight. Given that different UV imagers have different optimal imager gains, photon numbers under different gains could be calibrated to the values under optimal gains in accordance with the gain correction formula. Photon numbers decrease with the increase in the square of observation distance. Detection results under different observation distances could be corrected to the contrast distance after the detection of electrical equipment. The photon numbers of different UV imager types could be corrected in accordance with the instrumental correction factor. The results of this study can provide references to improve the applications and standardizations of UV imaging technology in corona discharge detection.

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Images Detection and Diagnosis of Corona Discharge on Porcelain Insulators at 22.9kV D/L

Cited by 22 publications

References 6 publications

Defect Diagnosis of Disconnector Based on Wireless Communication and Support Vector Machine

Defect Diagnosis of Disconnector Based on Wireless Communication and Support Vector Machine

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

Effects of Different Factors on Electrical Equipment UV Corona Discharge Detection

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