DocumentCode :
173626
Title :
Continuous, non-linear, optimal speed control of a Distributed Generation Power Pack using Artificial Neural Networks
Author :
Hill, Christopher Ian ; Zanchetta, Pericle ; Okaeme, Nnamdi A. ; Bozhko, Serhiy V.
Author_Institution :
Power Electron., Machines & Control Res. Group, Univ. of Nottingham, Nottingham, UK
fYear :
2014
fDate :
13-16 May 2014
Firstpage :
1050
Lastpage :
1055
Abstract :
Distributed Generation Power Packs with a combustion engine prime mover are still widely used to supply electric power in a variety of applications. These applications range from backup power supply systems to providing power in places where grid connection is either technically impractical or financially uneconomic. Due to the ever increasing cost of diesel fuel and the environmental issues associated with its use, the optimisation of these AC generators and the reduction of fuel consumption is vital. This paper presents how Artificial Neural Networks can be utilised in order to obtain a continuous function which relates variable load demand to optimal speed demand. The Artificial Neural Network toolbox within MATLAB is used to create, train and test the Artificial Neural Networks. This paper also shows the results of an experimental system used in order to emulate the Distributed Generation Power Pack. Overall it is shown that is possible to operate a variable speed system under optimal, non-linear, speed control using Artificial Neural Networks.
Keywords :
continuous systems; control engineering computing; distributed power generation; neural nets; nonlinear control systems; optimal control; optimisation; power engineering computing; power supplies to apparatus; power system stability; AC generator optimisation; MATLAB; artificial neural network; backup power supply system; combustion engine prime mover; continuous function; diesel fuel cost; distributed generation power pack; environmental issues; grid connection; nonlinear speed control; optimal speed control; optimal speed demand; variable load demand; Artificial neural networks; DC motors; Distributed power generation; Fuels; MATLAB; Optimization; Training; ANN; Distributed Generation; Optimal Control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Energy Conference (ENERGYCON), 2014 IEEE International
Conference_Location :
Cavtat
Type :
conf
DOI :
10.1109/ENERGYCON.2014.6850554
Filename :
6850554
Link To Document :
بازگشت