A Method for Substation Equipment Temperatue Prediction

The temperature change of the power transmission line and substation equipment can reflect their potential safety hazard caused by their aging and overload. Based on the nonlinear analysis of forecasting substation equipment temperature data can realize effectively early warning of equipment failure and avoid huge losses caused by the accident. This paper puts forward a method for temperature forecasting, based on the chaotic time series and BP neural network. It collects data from wireless temperature sensors to establish a time series of substation equipments’ temperature. Software simulation results showed that the prediction method has higher prediction accuracy than that of the traditional method.

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Structural Health and Stability Assessment of Qinghai–Tibet Power Transmission Line with Time-Series Insar Using X-Band Terrasar Data

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8899899 ◽

2019 ◽

Author(s):

Zhengjia Zhang ◽

Peng Fan ◽

Xiuguo Liu ◽

Mengmeng Wang ◽

Chao Wang ◽

...

Keyword(s):

Time Series ◽

Transmission Line ◽

Power Transmission ◽

Power Transmission Line ◽

Stability Assessment ◽

Structural Health ◽

X Band

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Research on power transmission line ice prediction system based on BP Neural Network

Proceedings of 2012 International Conference on Measurement, Information and Control ◽

10.1109/mic.2012.6273414 ◽

2012 ◽

Cited By ~ 1

Author(s):

Yi Luo ◽

Ying Li ◽

Yi Yao ◽

Xu Zhan

Keyword(s):

Neural Network ◽

Transmission Line ◽

Bp Neural Network ◽

Power Transmission ◽

Power Transmission Line ◽

Prediction System

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Chaotic Time Series Prediction Algorithm for Lorenz System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.2412 ◽

2014 ◽

Vol 513-517 ◽

pp. 2412-2415

Author(s):

Chen Zhang

Keyword(s):

Neural Network ◽

Time Series ◽

Bp Neural Network ◽

Lorenz System ◽

Prediction Method ◽

Chaotic Time Series ◽

Learning Ability ◽

Prediction Algorithm ◽

Swarm Optimization ◽

Glowworm Swarm Optimization

Based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm for BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to generate better network initial thresholds and weights so as to compensate the random defects for the thresholds and weights of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series generated by Lorenz system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so prove it is feasible and effective in the chaotic time series.

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Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

The Scientific World JOURNAL ◽

10.1155/2014/256815 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Peng Li ◽

Na Zhao ◽

Donghua Zhou ◽

Min Cao ◽

Jingjie Li ◽

...

Keyword(s):

Time Series ◽

Transmission Line ◽

Time Series Data ◽

Power Transmission ◽

Time Series Prediction ◽

Series Data ◽

Power Transmission Line ◽

Alarm Systems ◽

History Of ◽

Overhead Power Transmission Line

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

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Application of Improved BP Network in Failure Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.373 ◽

2012 ◽

Vol 490-495 ◽

pp. 373-377

Author(s):

Zhi Gang Li ◽

Bo Wei Shi

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Predictive Power ◽

Failure Prediction ◽

Prediction Method ◽

Equipment Failure ◽

Bp Network ◽

Neural Network Prediction ◽

Neural Network Algorithm ◽

Network Prediction

An improved BP neural network prediction method is used for collecting pipe equipment failure prediction and comparing with the improved BP neural network in front, which demonstrates that the improved BP neural network algorithm to the collecting pipe failures has better predictive power.

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Chaotic Time Series Prediction Method Based on BP Neural Network and Extended Kalman Filter

Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-03766-6_11 ◽

2018 ◽

pp. 100-108

Author(s):

Xiu-Zhen Zhang ◽

Li-Sang Liu

Keyword(s):

Neural Network ◽

Time Series ◽

Kalman Filter ◽

Extended Kalman Filter ◽

Bp Neural Network ◽

Time Series Prediction ◽

Prediction Method ◽

Chaotic Time Series ◽

Chaotic Time Series Prediction

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Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1096 ◽

2014 ◽

Vol 513-517 ◽

pp. 1096-1100

Author(s):

Yue Hou ◽

Hai Yan Li

Keyword(s):

Neural Network ◽

Time Series ◽

Bp Neural Network ◽

Time Series Prediction ◽

Prediction Method ◽

Chaotic Time Series ◽

Learning Ability ◽

Connection Weight ◽

Swarm Optimization ◽

Glowworm Swarm Optimization

In order to improve the neural network structure and setting method of parameters, based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm of BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to obtain better network initial threshold and weight so as to compensate the defect of connection weight and thresholds choosing of BPNN, thus BPNN can have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series of tent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series.

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