Online prediction method of icing of overhead power lines based on support vector regression

Li, Jingjie; Li, Peng; Miao, Aimin; Chen, Yong; Cao, Min; Shen, Xin

doi:10.1002/etep.2500

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Yang [18]. Li et al used historical icing data and online meteorological data and adopted the support vector regression algorithm to construct an icing early warning system that could predict the short-term icing load of overhead transmission lines [19]. The icing of transmission lines changes slowly with time, and the icing sequence data has the characteristics of time series and autocorrelation.…”

Section: Related Workmentioning

confidence: 99%

Transmission line icing forecasting based on characteristic graph structure

Wen

Gao

et al. 2023

Second International Conference on Digital Society and Intelligent Systems (DSInS 2022)

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Icing of transmission lines has always been a pain point for grid companies. The economic and property losses caused by icing every winter are huge. How to make an effective prediction of transmission line icing is a difficult problem. Existing forecasting methods are often based on micro-meteorological and micro-topographic information. In the characteristic variables of micro-meteorology and micro-topography, there are often interdependencies and potential spatial correlations. However, existing icing prediction methods do not fully exploit the interactions among these characteristic variables. Therefore, this paper proposes a transmission line icing prediction model based on the feature map structure, which reveals the potential agnostic topological relationship between the feature variables by adaptively extracting the sparse adjacency matrix between the feature variables. In addition, while the dilated convolution can improve the receptive field, there is also a loss of information continuity due to the discontinuity of the convolution kernel of the dilated convolution. We propose a temporal capture module to improve the loss of information continuity through GRU and dilated convolution in parallel. End-to-end prediction is achieved by stacking a graph convolution module and a temporal capture module, and after conducting several experimental comparisons, the effective prediction of the proposed model is validated.

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Section: Related Workmentioning

confidence: 99%

Transmission line icing forecasting based on characteristic graph structure

Wen

Gao

et al. 2023

Second International Conference on Digital Society and Intelligent Systems (DSInS 2022)

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“…However, the icing process for the power transmission lines has a nonperiodic motion and stronger sensitivity. In reference [16], the chaotic characteristics of the time series of the icing load are verified by calculating the largest Lyapunov exponent. The PSR method is used in this paper to analyze the intrinsic attributes of icing load changes.…”

Section: Psrmentioning

confidence: 99%

Icing Load and Risk Forecasting for Power Transmission Line Based on Multi-scale Time Series Phase-Space Reconstruction and Regression

Chen¹,

Wang²,

Ren³

et al. 2021

IJSSE

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Accurately forecasting the icing load on overhead power transmission lines is an important issue to ensure the security and reliability of the power grid. A multi-scale time series phase-space reconstruction and regression model for icing load prediction is proposed in this paper to treat the non-stationary, nonlinear, and intermittent volatility of power line icing load data. Those is motivated by the traditional icing load prediction models having many disadvantages in the forecasting accuracy, as well as the casualness of the parameters selected. Firstly, the icing load data are decomposed into a multi-scale time series of intrinsic model function (IMF) components with stability by using the ensemble empirical mode decomposition (EEMD), which can reduce the interactions between different types of feature information. Secondly, phase-space reconstruction (PSR) theory is applied using the mutual information and the false nearest neighbor to determine the optimal delay time and embedding dimension of each IMF component. Thirdly, considering the characteristics of each IMF component, different kernel functions and optimization parameters are selected to establish the prediction model based support vector regression (SVR). Finally, according to the load prediction results, fuzzy reasoning method was used to determine the risk status of transmission line towers in this paper. Upon experimentally evaluating the validity of the model using related transmission lines of the Yunnan Power Grid, it is shown that this method could predict the real-time icing load on overhead power lines, obtaining better regression performance. This model could be used on power transmission and distribution systems for deicing and maintenance decisions.

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