Sine-fitting multiharmonic algorithms implemented by artificial neural networks
A new method designed to perform high-accuracy spectral analysis, based on ADALINE artificial neural networks (ANNs), is proposed. The proposed network is able to accurately calculate the fundamental frequency and the harmonic content of an input signal. The method is especially useful in high-precision digital measurement systems in which periodical signals are involved, i.e. digital watt meters. Most of these systems use spectral analysis algorithms as an intermediate step for the computation of the magnitudes of interest. The traditional spectral analysis methods require synchronous sampling, which introduce limitations to the sampling circuitry. Sine-fitting multiharmonics algorithms resolve the hardware limitations concerning the synchronous sampling but have some limitations with regard to the phase of the array of samples. The new implementation of sine-fitting multiharmonics algorithms based on ANN eliminates these limitations.
harmonics spectral analysis adaline ann digital measurement sine-fitting methods discrete fourier transform