Classification of power system disturbances through fuzzy neural network

Author

Damarla, G.P. ; Chandrasekaran, A. ; Sundaram, Ashok

Author_Institution

Tennessee Technol. Univ., Cookeville, TN, USA

fYear

1994

fDate

25-28 Sep 1994

Firstpage

68

Abstract

Electric power utilities have launched comprehensive data collection programs to evaluate the power quality problems in their systems. Computerized classification and characterization of the data will help in dealing with voluminous quantities of monitored data. An artificial neural network (ANN) approach with and without a fuzzy system for classifying the disturbances in a power system is developed and tested. The results obtained demonstrate the power of neural networks in classifying the commonly encountered disturbances of sags, swells, waveform distortions, interruptions and impulses and the effect of the fuzzy system on the network

Keywords

data acquisition; data analysis; electrical faults; feedforward neural nets; fuzzy control; fuzzy neural nets; power supply quality; power system analysis computing; power system control; artificial neural network; data characterization; data classification; data collection programs; electric power utilities; feedforward neural nets; fuzzy logic controller; fuzzy neural network; impulses; interruptions; power quality problems; power system disturbances classification; sags; swells; waveform distortions; Data acquisition; Feedforward neural networks; Fuzzy control; Fuzzy neural networks; Neural network applications; Power quality; Power system control; Power system faults; Power system simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1994. Conference Proceedings. 1994 Canadian Conference on

Conference_Location

Halifax, NS

Print_ISBN

0-7803-2416-1

Type

conf

DOI

10.1109/CCECE.1994.405659

Filename

405659