A Model Based on BP Neural Network for Audible Noise Prediction

You Xian Peng; Bo Tang; Hong Ying Cao; Bin Chen; Yu Li

doi:10.4028/www.scientific.net/AMR.986-987.1356

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 986-987A Model Based on BP Neural Network for Audible...

A Model Based on BP Neural Network for Audible Noise Prediction

Abstract:

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.

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Periodical:

Advanced Materials Research (Volumes 986-987)

Pages:

1356-1359

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.986-987.1356

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Online since:

July 2014

Authors:

You Xian Peng, Bo Tang*, Hong Ying Cao, Bin Chen, Yu Li

Keywords:

AC Transmission Lines, Audible Noise, Average Absolute Error, BP Neural Network (BPNN), Prediction Model

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