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An Improved Wavelet Neural Network Harmonic Detection Method
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This study discusses the power quality of heavy industry at MIDC area in Pune city of Maharashtra state (India). Power quality issues are rapidly increasing in electrical distribution networks due to industrial revolution. Use of nonlinear devices and heavy load equipments is increasing with industrial revolution. Also due to industrialization the rate of environmental pollution and electrical pollution is also growing. This study examines and measures the electrical pollution associated with heavy mechanical industry. Power quality analysis is conducted with application of power quality analyzer in mechanical industry. The input side of the loads is considered for various required measurements for the presented study. The objective of this paper is to find out the harmonics contents in mechanical industry, analyze readings and determine the level of harmonics in voltage and current.
There are difficulties in performing synchronized sampling and integral period truncation in the harmonic analysis of power system with the fast Fourier transform (FFT) technique. Some efforts have been made including the utilization of window functions and interpolation algorithms to correct the measured frequency, phase and amplitude by FFT. In order to overcome some shortcomings of existing correction methods, an improved algorithm is present in this paper, with which the amplitudes of harmonics can be estimated from the two neighboring spectral lines. The polynomial approximation method is also employed to obtain simple formulas for frequency and amplitude correction. By these methods, the disturbance of the frequency leakage and the noise can be reduced and the accuracy of the harmonic analysis can be improved. Based on the proposed algorithm, the practical correction formulas for some typical window functions are developed. The simulation results have verified the effectiveness and practicability of the algorithm.
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