Title :
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
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;
Conference_Titel :
Electrical and Computer Engineering, 1994. Conference Proceedings. 1994 Canadian Conference on
Conference_Location :
Halifax, NS
Print_ISBN :
0-7803-2416-1
DOI :
10.1109/CCECE.1994.405659
