scholarly journals Comparison on Wind Load Prediction of Transmission Line between Chinese New Code and Other Standards

2011 ◽  
Vol 14 ◽  
pp. 1799-1806 ◽  
Author(s):  
JIANG Qi ◽  
DENG Hongzhou
Keyword(s):  
Transmission Line ◽  
Wind Load ◽  
Load Prediction

Effects of static wind load on the vibration of transmission line

2020 ◽  
Author(s):  
Tao Yi ◽  
Changzhao Qian ◽  
Guangwei Zhou ◽  
Jiceng Han ◽  
Deyuan Lin ◽  
...  
Keyword(s):  
Transmission Line ◽  
Wind Load

scholarly journals Wind load prediction on single tree with integrated approach of L-system fractal model, wind tunnel, and tree aerodynamic simulation

AIP Advances ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 075202
Author(s):  
Hee Joo Poh ◽  
Woei Leong Chan ◽  
Daniel J. Wise ◽  
Chi Wan Lim ◽  
Boo Cheong Khoo ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Integrated Approach ◽  
Fractal Model ◽  
Wind Load ◽  
Load Prediction ◽  
Single Tree ◽  
L System ◽  
Aerodynamic Simulation

scholarly journals Damage Probability Assessment of Transmission Line-Tower System Under Typhoon Disaster, Based on Model-Driven and Data-Driven Views

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1447 ◽  
Author(s):  
Hui Hou ◽  
Hao Geng ◽  
Yong Huang ◽  
Hao Wu ◽  
Xixiu Wu ◽  
...  
Keyword(s):  
Transmission Line ◽  
Power Grid ◽  
Wind Load ◽  
Data Driven ◽  
Probability Assessment ◽  
Model Driven ◽  
Damage Probability ◽  
Typhoon Disaster ◽  
Damage Probability Assessment ◽  
Tower System

Under the typhoon disaster, the power grid often has serious accidents caused by falling power towers and breaking lines. It is of great significance to analyze and predict the damage probability of a transmission line-tower system for disaster prevention and reduction. However, some problems existing in current models, such as complicated calculation, few factors, and so on, affect the accuracy of the prediction. Therefore, a damage probability assessment method of a transmission line-tower system under a typhoon disaster is proposed. Firstly, considering the actual wind load and the design wind load, physical models for calculating the damage probability of the transmission line and power tower are established, respectively based on model-driven thought. Then, the damage probability of the transmission line-tower system is obtained, combining the transmission line and power tower damage probability. Secondly, in order to improve prediction accuracy, this paper analyzes the historical sample data containing multiple influencing factors, such as geographic information, meteorological information, and power grid information, and then obtains the correction coefficient based on data-driven thought. Thirdly, the comprehensive damage probability of the transmission line-tower system is calculated considering the results of model-driven and data-driven thought. Ultimately, the proposed method is verified to be effective, taking typhoon ‘Mangkhut’ in 2018 as a case study.

A pounding spacer damper and its application on transmission line subjected to fluctuating wind load

2016 ◽  
Vol 24 (8) ◽  
pp. e1950 ◽  
Author(s):  
Xing Fu ◽  
Hong-Nan Li ◽  
Jia-Xiang Li ◽  
Peng Zhang
Keyword(s):  
Transmission Line ◽  
Wind Load ◽  
Fluctuating Wind

WIND LOAD PREDICTION OF LARGE-SPAN DRY COAL SHEDS BASED ON GRNN AND ITS APPLICATION

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Ying Sun ◽  
Lin Yang ◽  
Yue Wu
Keyword(s):  
Wind Tunnel ◽  
Prediction Models ◽  
Peak Frequency ◽  
Wind Load ◽  
Wind Pressure ◽  
Generalized Regression Neural Network ◽  
Load Prediction ◽  
Spectral Parameters ◽  
Wind Tunnel Tests ◽  
Large Span

The distribution and fluctuation of wind load on large-span dry coal sheds are complicated. Wind load on typical shape of roofs can be sometimes determined based on the wind tunnel tests carried out on roofs of similar shape. To expand the application scope of the test data, Generalized Regression Neural Network (GRNN) is introduced. The prediction models on large-span dry coal are given, where the wind load is expressed by eight parameters: mean, RMS, skewness, kurtosis of wind pressure coefficients, three auto-spectral parameters (including descendent slope in high frequency range, peak reduced spectrum and reduced peak frequency) and coherence exponent for cross-spectra. Cross validation and trails are carried out to determine the parameter in the GRNN model. Further, the wind load prediction is applied on a dry coal shed shell. The wind-induced responses are calculated and compared with the results of wind tunnel tests, with extremely close result. Therefore, it can be concluded that GRNN is feasible in predicting wind load on roof structures.

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

2021 ◽  
Vol 11 (1) ◽  
pp. 79-90
Author(s):  
Yong Chen ◽  
Peng Li ◽  
Huan Wang ◽  
Wenping Ren ◽  
Min Cao
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Transmission Line ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Grid ◽  
Phase Space Reconstruction ◽  
Kernel Functions ◽  
Load Prediction ◽  
Multi Scale

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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