Machine learning-based ice detection approach for power transmission lines by utilizing FBG micro-meteorological sensors

Zhou, Rui; Zhang, Zhiguo; Zhai, Tianrui; Gu, Xueliang; Cao, Huiran; Xiao, Zuoxiang; Li, LuMing

doi:10.1364/oe.477309

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…In recent years, researchers have focused on exploring the inherent relationship between meteorological elements and power transmission line icing rates. To achieve this, they have employed various machine learning techniques, including neural networks, support vector machines, gray correlation, and particle swarm optimization for parameter tuning [98][99][100][101][102]. These techniques have been utilized to construct a series of machine learning prediction models.…”

Section: Artificial Intelligence Prediction Modelsmentioning

confidence: 99%

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The Causes and Forecasting of Icing Events on Power Transmission Lines in Southern China: A Review and Perspective

Wang,

Chen,

Zhang

et al. 2023

Atmosphere

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The icing on power transmission lines, as a major hazard affecting the safety of electricity usage in China during winter, poses a significant challenge in systematically evaluating the weather conditions and their distribution characteristics during the icing period. Understanding the interaction between the microterrain and micrometeorology and achieving a refined analysis of the physical mechanisms during the icing process remain difficult tasks in this field. These are crucial aspects for the development of more accurate icing prediction models across southern China. Therefore, this study provides a comprehensive review and summary of the current research state and progress in the study of power transmission line icing in southern China from three perspectives: (1) large-scale circulation characteristics; (2) microphysical process, terrain–atmosphere interaction, microtopography and local micrometeorological conditions for the occurrence of icing events; and (3) numerical icing event modeling and forecasting. This study also looks ahead to the scientific issues and technological bottlenecks that need to be overcome for the prediction of ice coating on power transmission lines in southern China. The goal is to provide guidance for the causal analysis and forecasting warnings of power transmission line icing in the complex microterrain of the southern region.

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Section: Artificial Intelligence Prediction Modelsmentioning

confidence: 99%

“…A machine learning prediction model built through the intrinsic correlation between meteorological elements and ice accretion rates on power lines [98][99][100][101][102][103][104].…”

Section: Artificial Intelligence Prediction Modelmentioning

confidence: 99%

The Causes and Forecasting of Icing Events on Power Transmission Lines in Southern China: A Review and Perspective

Wang,

Chen,

Zhang

et al. 2023

Atmosphere

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“…Ice-covered transmission lines in winter are a common hazard. After the transmission lines freeze, the significant increase in the weight of the cables can exceed their load-bearing capacity, causing cable deformation, and potentially leading to the collapse of the towers at both ends of the transmission lines [3][4][5]. Severe ice-covered transmission lines affect the normal operation of the transmission system and bring great inconvenience to daily life.…”

Section: Introductionmentioning

confidence: 99%

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness

Zhang,

Dou,

Jiao

et al. 2024

Sensors

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Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder’s ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

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“…IOP Publishing doi:10.1088/1742-6596/2785/1/012071 2 Existing standards have mainly standardized the composition, functional requirements, technical requirements, and testing methods of the meteorological monitoring devices for transmission lines, but have not yet stipulated the layout principles and data processing methods for the meteorological monitoring devices [6][7][8][9]. On this background, there is an urgent need for a layout method of meteorological monitoring points for transmission lines, which can make comprehensive meteorological monitoring of transmission lines with limited monitoring points.…”

Section: Introductionmentioning

confidence: 99%

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Zhang,

Wang,

Jiang

et al. 2024

J. Phys.: Conf. Ser.

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To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

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Machine learning-based ice detection approach for power transmission lines by utilizing FBG micro-meteorological sensors

Cited by 7 publications

References 17 publications

The Causes and Forecasting of Icing Events on Power Transmission Lines in Southern China: A Review and Perspective

The Causes and Forecasting of Icing Events on Power Transmission Lines in Southern China: A Review and Perspective

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

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