Insulator Detection for High-Resolution Satellite Images Based on Deep Learning

Zhou, Fangrong; Jin, Weishi; Zheng, Zezhong; Mou, Fan; Li, Zhongnian; Ma, Yutang; Bu, Wei; Huang, Shuangde; Wang, Qun

doi:10.1109/lgrs.2023.3251372

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Given the need to keep the electric power system running, techniques for the maintenance and prediction of insulator failure are employed by electric power utilities [9]. One of the most-common techniques is visual inspection, which can be further improved using thermographic cameras [10], ultraviolet light detectors [11], ultrasound signals [12], radio interference, acoustic techniques [13], unmanned aerial vehicles [14], and leakage current techniques [15]. The maintenance is carried out by field technicians, which, when detecting possible defective insulators, perform the cleaning or the replacement of the insulator [16].…”

Section: Related Workmentioning

confidence: 99%

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Stefenon

Seman

Neto

et al. 2023

Sensors

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Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.

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Section: Related Workmentioning

confidence: 99%

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Stefenon

Seman

Neto

et al. 2023

Sensors

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“…For instance, Wei et al [7] used nodes and lines to build an insulator string model and proposed a chain network for training to identify the rotating bounding box of insulators from drone images; Chen et al [8] proposed a YOLOv5-3S-4PH model based on the fusion of lightweight networks and enhanced multi-scale features to identify insulator defects. Using SuperView-3 and WorldView-1 satellite images as the basis, Zhou et al [9] engineered three deep learning models for the purpose of enhancing image resolution, identifying towers, and detecting insulators. These methods require high-resolution and high-quality images.…”

Section: Introductionmentioning

confidence: 99%

Insulator Extraction from UAV LiDAR Point Cloud Based on Multi-Type and Multi-Scale Feature Histogram

Chen,

Li,

Pan

et al. 2024

Drones

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Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s.

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Insulator Detection for High-Resolution Satellite Images Based on Deep Learning

Cited by 2 publications

References 18 publications

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Insulator Extraction from UAV LiDAR Point Cloud Based on Multi-Type and Multi-Scale Feature Histogram

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