Power disturbance identification through pattern recognition system

Author

Chai, Soon-Kin ; Sekar ; Rajan

Author_Institution

Dept. of Electr. & Comput. Eng., Tennessee Technol. Univ., Cookeville, TN, USA

fYear

2000

fDate

2000

Firstpage

154

Lastpage

157

Abstract

This paper presents an artificial intelligent system to identify and classify the power disturbance waveforms that are obtained from the monitoring system in a power control station. The pattern recognition technique used in this paper is a combination of Bayes´ linear classifier and artificial neural network (ANN). Simulated disturbance waveforms are transformed by the fast Fourier transformation and the feature vector is extracted. The weight matrix for ANN is generated by the linear classifier and fed into ANN. The product of the test sample and the weight matrix will be the input of the ANN. The system can identify the power disturbance and it can provide the power surge frequency as well

Keywords

Bayes methods; fast Fourier transforms; pattern classification; pattern recognition; power system faults; power system simulation; surges; Bayes´ linear classifier; artificial intelligent system; fast Fourier transformation; feature vector; linear classifier; monitoring system; pattern recognition system; power control station; power disturbance; power disturbance identification; power disturbance waveforms; power surge frequency; simulated disturbance waveforms; weight matrix; Artificial intelligence; Artificial neural networks; Feature extraction; Intelligent systems; Monitoring; Pattern recognition; Power control; Surges; Testing; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Southeastcon 2000. Proceedings of the IEEE

Conference_Location

Nasville, TN

Print_ISBN

0-7803-6312-4

Type

conf

DOI

10.1109/SECON.2000.845454

Filename

845454