DocumentCode :
3263732
Title :
Artificial neural networks application for current rating of overhead lines
Author :
Negnevitsky, Michael ; Le, Tan LOC
Author_Institution :
Dept. of Electr. & Electron. Eng., Tasmania Univ., Hobart, Tas., Australia
Volume :
1
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
418
Abstract :
This paper describes an application of an intelligent system consisting of an expert system and artificial neural networks (ANN) for the evaluation of the thermal rating and temperature rise of overhead power lines. The hourly solar irradiance is determined by the ANN and regression best-fitting techniques. The neural network was trained for the prediction of hourly or instantaneous values of the irradiance dependent on astronomic and meteor-climatic conditions. The developed intelligent system can be used to assist operators in loading of power transmission lines in different operating, ambient, cloud and ground reflection conditions. It can also assist the operators to determine the permissible duration of the conductor overload
Keywords :
expert systems; feedforward neural nets; learning (artificial intelligence); power engineering computing; power overhead lines; power transmission lines; conductor overloading; expert system; feedforward neural networks; generalised delta rule network; intelligent system; overhead power lines; regression best-fitting; solar radiation; temperature rise; thermal rating; Ambient intelligence; Artificial intelligence; Artificial neural networks; Clouds; Expert systems; Intelligent networks; Intelligent systems; Power overhead lines; Power transmission lines; Temperature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.488137
Filename :
488137
Link To Document :
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