The Combination of Testing and 1D Modeling for Booming Noise Prediction in the Model Based System Testing Framework

Author(s):  
Fábio Luis Marques dos Santos ◽  
Tristan Enault ◽  
Jan Deleener ◽  
Tom Van Houcke ◽  
Herman Van der Auweraer
2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.


Author(s):  
Miguel Pinto ◽  
Marcelo Gonçalves ◽  
Paolo Masci ◽  
José Creissac Campos

2017 ◽  
Vol 143 (6) ◽  
pp. 04017008 ◽  
Author(s):  
Nahyun Kwon ◽  
Moonseo Park ◽  
Hyun-Soo Lee ◽  
Joseph Ahn ◽  
Sooyoung Kim

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