Title :
Intelligent neural network based dynamic power system harmonic analysis
Author_Institution :
Dept. of Autom. Eng., Chien Kuo Technol. Univ., Taiwan
Abstract :
Nowadays, power harmonic problems have attracted more attention in power systems. Traditional technical tools for harmonic distortion analysis using either FFT or DFT are, however, susceptible to the presence of noise and sub-harmonics in the distorted signals. In this paper, an alternative method using back propagation neural network algorithm has achieved satisfactory results for dynamic harmonic distortion analysis in noisy environments. Sensitivity considerations are also conducted to determine the key factors affecting the performance efficiency of the proposed model to reach the lowest errors of testing patterns.
Keywords :
backpropagation; discrete Fourier transforms; harmonic analysis; harmonic distortion; neural nets; power engineering computing; power system harmonics; DFT; FFT; back propagation neural network algorithm; dynamic power system harmonic analysis; harmonic distortion analysis; intelligent neural network; Algorithm design and analysis; Harmonic analysis; Harmonic distortion; Intelligent networks; Neural networks; Power system analysis computing; Power system dynamics; Power system harmonics; Signal analysis; Working environment noise;
Conference_Titel :
Power System Technology, 2004. PowerCon 2004. 2004 International Conference on
Print_ISBN :
0-7803-8610-8
DOI :
10.1109/ICPST.2004.1460000