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