Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

Zhai, Yongjie; Cheng, H. D.; Chen, Rui; Yang, Qiang; Li, Xiaoxia

doi:10.3390/en11020340

Cited by 32 publications

(18 citation statements)

References 20 publications

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“…28. [72] The surface fault of insulator IULBP NP IULBP+Rules Texture [73] Missing-cap of insulator GLCM CGT-LBP-HSV GLCM+Rules Texture [74] The surface fault of insulator GSS-GSO GrabCut Rules Shape [75] Missing-cap of insulator Up-Net+CNN Up-Net CNN Deep [76] The surface fault of insulator M-SA F-PISA Colour model Colour [77] Missing-cap of insulator SMF Colour model Morphology Fusion [78] The surface fault of insulator CGL-EGL CGL EGL Shape [79] The surface fault of insulator M-PDF OAD-BSPK AlexNet Deep [80] Missing…”

Section: Inspection Of Power Linesmentioning

confidence: 99%

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

et al. 2021

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Deep learning (DL) is an exciting field of interest for many researchers and business. Due to a massive leap in DL based research, many domains like Business, science and government sectors make use of DL for various applications. This work puts forward the importance of DL and its application in a few critical electrical segments. Initially, an introduction to Artificial Intelligence (AI) and Machine Learning (ML) is presented. Then the need for DL and the popular architectures, algorithms and frameworks used are presented. A summary of different techniques used in DL is outlined, and finally, a review on the application of deep learning techniques in some popular electrical applications is presented. Five critical electrical applications, namely identification of bearing faults, hot spots on the surface of PV panels, insulator faults, an inspection of power lines and Electric vehicles have been considered for review in this work. The primary aim of this work is to present chronologically, a survey of different areas in which it applies DL along with their architectures, frameworks and techniques to provide a deeper understanding of DL for widespread use in real-time applications.

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Section: Inspection Of Power Linesmentioning

confidence: 99%

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

et al. 2021

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“…For the insulator string detection and insulator faults detection in aerial images, many vision-based methods have been proposed to pursue automatic detection that could be used in on-line or off-line applications [14]. Some typical insulator faults, such as missing [3], flashover [15], and contamination [16], have attracted widespread attention. Specifically, the missing faults are considered to be the most severe insulator fault if they cannot be detected and then repaired in time.…”

Section: Introductionmentioning

confidence: 99%

Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Han

Yang

et al. 2020

Energies

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Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.

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“…One of the most salient features of the proposed scheme is the ability to detect 17 different types of electrical equipment. Moreover, most of the existing methods present naïve approaches of thresholding the color or intensity image using a single threshold [ 1 , 14 , 15 , 16 , 17 , 25 , 26 , 33 , 34 , 35 , 36 ], and hence these methods are sensitive to color and lighting variations. In order to show the robustness of the our proposed method, we evaluated our method on a large dataset of 644 cluttered insulator images, while the methods described by [ 17 , 22 , 24 , 26 , 33 , 36 ] used small datasets of 2, 3, 4, 5, 10, and 74 images, respectively, and the methods in [ 2 , 21 ] tested their algorithm on a single image.…”

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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Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

Cited by 32 publications

References 20 publications

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network

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