Geometric Nonlinear Analysis of Self-Supporting Structures for Overhead Transmission Lines

Reis, Elvys Dias; Carvalho, Hermes; Oliveira, Camila Cordeiro de; Vasconcelos, João Antônio de; Adriano, Ricardo; Lopes, Danilo Campos

doi:10.1061/ppscfx.sceng-1236

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Therefore, they are highly susceptible to derailment and overturning [3][4][5], which can lead to significant casualties and huge economic losses [6,7]. Meanwhile, safety and reliability of structures (e.g., bridges and transmission Lines) under the action of strong winds have also become an increasingly serious topic [8][9][10][11][12][13][14][15]. One of effective methods to addressing this predicament is the accurate prediction of strong winds (i.e., short-term wind speed prediction), which will enable the railway dispatching system to adjust dispatch plans in a timely manner [16].…”

Section: Introductionmentioning

confidence: 99%

An Improved Ensemble-Strategy-Assisted Wind Speed Prediction Method for Railway Strong Wind Warnings

Liu,

Cui,

Cheng

et al. 2023

Atmosphere

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Reliable short-term wind speed prediction is one of the core technologies in the strong wind warning system for railway applications, which is of great significance for ensuring the safety of high-speed train operations and ancillary railway facilities. To improve forecasting accuracy, decomposition-based methods have attracted extensive attention due to their superior ability to address complex data characteristics (e.g., nonstationarity and nonlinearity). Currently, there are two pre-processing schemes for decomposition-based methods, i.e., one-time decomposition and real-time decomposition. In order to apply them better, this paper first expounds the difference between them, based on a combination of DWT (discrete wavelet transform) and CKDE (conditional kernel density estimation). The results show that although the one-time decomposition-based method has an unexceptionable accuracy, it only can provide offline prediction and thus may not be practical. The real-time decomposition-based method possesses stronger practicability and is able to provide online prediction, but it has limited accuracy. Then, an improved ensemble strategy is developed by optimizing the selection of appropriate decomposed components to conduct the prediction on the basis of real-time decomposition. This improved ensemble strategy provides an effective guidance for this selective combination, including taking historical information into consideration in the data. Finally, numerical examples and practicality analysis using two groups of measured wind speed data demonstrate that the proposed method is effective in providing high-precision online wind speed prediction. For example, compared with CKDE, the average degrees of improvement achieved by the proposed method in terms of MAE, RMSE, and MRPE, are 16.25%, 17.66%, and 16.93, respectively, while those compared with the traditional real-time decomposition method are 17.11%, 18.54%, and 16.84, respectively.

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