Analysis of transmission line icing prediction based on CNN and data mining technology

Li, Lixue; Luo, Da; Yao, Wenhao

doi:10.1007/s00500-022-06812-7

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Han et al introduced an ISSA-CNN-LSTM model for predicting transmission line icing thickness, utilizing an improved sparrow search algorithm, convolutional neural network, and long short-term memory to address the challenge of accurate ice thickness forecasting in power systems [29]. In terms of the damage caused by electric power line icing, Li et al introduced a CNN and Spark large data platform-based ice data preprocessing framework and comprehensively analyzed the impact of meteorological data on ice cover thickness [30]. Wen et al presented a Gated Temporal Convolutional Network (GTCN) enhanced by a multi-source information fusion scheme to predict transmission line icing tension, addressing the complexity introduced by various meteorological factors and line attributes [31].…”

Section: Introductionmentioning

confidence: 99%

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ke,

Sun,

Zhao

et al. 2024

Electronics

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Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensure accurate and efficient prediction of transmission line icing thickness. We address this challenge by leveraging the meteorological features of icing phenomena and propose a novel feature preprocessing method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral clustering. This method effectively preprocesses raw time series data, extracts key features, and constructs a similarity matrix and feature vector. The resulting feature vector serves as a new data representation, facilitating cluster analysis to isolate meteorological and icing-related features specific to transmission lines. Subsequently, we introduce an enhanced Transformer model for predicting transmission line icing thickness. The proposed model leverages the extracted meteorological and icing features by independently embedding variable tokens for each input feature. This approach improves the model’s prediction accuracy under multiple feature inputs, leading to more effective learning. The experimental results demonstrate that the performance of the proposed prediction algorithm is better than the three baseline algorithms (hybrid CEEMDAN and LSTM (CEEMDAN-LSTM), hybrid CEEMDAN, spectral clustering, and LSTM (CEEMDAN-SP-LSTM), and hybrid CEEMDAN, spectral clustering, and Transformer (CEEMDAN-SP-Transformer)) under multiple feature inputs and different parameter settings.

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Section: Introductionmentioning

confidence: 99%

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ke,

Sun,

Zhao

et al. 2024

Electronics

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“…The icing disaster of transmission lines is one of the most serious threats to the safe operation of the power system. The harsh climate and traffic conditions during an ice disaster make the repair process extremely difficult, resulting in a long outage time and greater economic losses and social impact [1][2][3][4]. According to the statistics of the power sector, China's transmission line failures caused by icing have reached nearly a thousand since 1950.…”

Section: Introductionmentioning

confidence: 99%

A Superhydrophobic Anti-Icing Surface with a Honeycomb Nanopore Structure

Li,

Xiang,

Dai

et al. 2023

Coatings

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Recently, the icing disaster of transmission lines has been a serious threat to the safe operation of the power system. A superhydrophobic (SHP) anti-icing surface with a honeycomb nanopore structure was constructed using anodic oxidation technology combined with a vacuum infusion process. When the current density was 87.5 mA/cm2, the honeycomb porous surface had the best superhydrophobic performance (excellent water mobility), lowest ice-adhesion strength (0.7 kPa) and best anti-frosting performance. Compared with other types of alumina surfaces, the ice-adhesion strength of the SHP surface (87.5 mA/cm2) was only 0.2% of that of the bare surface. The frosting time of the SHP surface (87.5 mA/cm2) was 150 min, which was much slower. The former is attributed to the air cushion within the porous structure and the stress concentration, and the latter is attributed to the self-transition of the droplets and low solid–liquid heat transfer area. After 100 icing or frosting cycles, the SHP surface (87.5 mA/cm2) maintained a low ice-adhesion strength and superhydrophobic performance. This is because the anodic oxidation process forms a hard porous film, and the nano porous structure with a high aspect ratio can store modifiers to realize self-healing. The results indicate that the SHP surface with a honeycomb nanopore structure presents excellent anti-icing performance and durability.

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Analysis of lightning arrester operating current based on multidimensional neural network for transmission lines

Yang¹,

He²,

Chen³

et al. 2022

Evol. Intel.

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Analysis of transmission line icing prediction based on CNN and data mining technology

Cited by 8 publications

References 12 publications

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

Ice Cover Prediction for Transmission Lines Based on Feature Extraction and an Improved Transformer Scheme

A Superhydrophobic Anti-Icing Surface with a Honeycomb Nanopore Structure

Analysis of lightning arrester operating current based on multidimensional neural network for transmission lines

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