Automatic reactive power control of wind-diesel-micro-hydro autonomous hybrid power systems using ANN tuned static VAr compensator

Author

Bansal, R.C. ; Bhatti, T.S. ; Kothari, D.P.

Author_Institution

Electr. & Electron. Eng., Biria Inst. of Technol. & Sci., Rajasthan, India

fYear

2003

fDate

7-9 May 2003

Firstpage

182

Lastpage

188

Abstract

This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the static VAr compensator (SVC) controller for controlling the reactive power of variable slip/speed isolated wind-diesel-micro-hydro hybrid power systems. Transient responses of sample hybrid power system have been presented.

Keywords

backpropagation; diesel-electric generators; feedforward neural nets; hybrid power systems; hydroelectric power stations; multilayer perceptrons; power system control; reactive power control; static VAr compensators; transient response; wind power plants; ANN tuned static VAr compensator; SVC reactive power controller; artificial neural network; automatic reactive power control; error back-propagation training; multi-layer feed-forward ANN; reactive power; transient responses; wind-diesel-micro-hydro autonomous hybrid power systems; Artificial neural networks; Automatic control; Control systems; Error correction; Feedforward systems; Hybrid power systems; Load modeling; Power system modeling; Reactive power control; Static VAr compensators;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering, 2003 Large Engineering Systems Conference on

Print_ISBN

0-7803-7863-6

Type

conf

DOI

10.1109/LESCPE.2003.1204701

Filename

1204701