Title :
Dynamic power system harmonic detection using neural network
Author_Institution :
Dept. of Autom. Eng., Chien Kuo Technol. Univ., Hua City
Abstract :
Conventional approaches for harmonics measurement usually employ either FFT or DFT. They, however, are susceptible to the presence of noise in the distorted power line. This paper proposes an alternative neural network based algorithm to detect the location of dynamic power harmonics in noisy environments. Sensitivity considerations are also conducted to determine the key parameters that affect the model performance efficiency in the lowest errors of testing patterns
Keywords :
harmonic distortion; neural nets; power engineering computing; power system harmonics; artificial neural network; distorted power line; dynamic power system harmonic detection; noise environment; Artificial neural networks; Continuous wavelet transforms; Discrete Fourier transforms; Neural networks; Neurons; Power harmonic filters; Power system dynamics; Power system harmonics; Signal processing algorithms; Wavelet transforms;
Conference_Titel :
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-8643-4
DOI :
10.1109/ICCIS.2004.1460683