Parameters Estimation Algorithm For Electrical Harmonics And Interharmonics

With extensive applications of nonlinear or impact loads in power system, that causes serious harmonics contamitation to the electric network. There are not only integer harmonics but also many non-integer harmonics which are interharmonics in modern electric network. The accurate estimation of harmonics and interharmonics parameters is the premise for their compensation. The paper mainly investigated the parameters estimation algorithm of harmonics and interharmonics in Fourier transform, wavelet transform, modern spectral estimation and Adaline neural network.Fourier transform is the most elementary method for the analysis of harmonics and interharmonics, which has been adopted by IEC 61000-4-7 standard. The disadvantage of applying Fourier transform to harmonics analysis is that it exists the spectral leakage and fence effect, its frequency resolution is inversely proportional to the sample data length in asynchronous sampling. The windowed interpolating Fourier transform is the effective method to restrain spectral leakage and eliminate fence effect, however there are conflicts between the measurement precision and the computational burden for the current windowed interpolating Fourier transform methods. For example, Hanning window interpolating algorithm is simple, but its accuracy is low; Blackman-Harris window interpolating algorithm has high precision, but its computational amount is large. Therefore, the interpolating Fourier transform algorithm and its improved strategy based on three-term third derivative and four-term fifth derivative Nuttall window were proposed in this paper. The solution of their interpolating coefficients is simple, while the analytical accuracy of harmonics are comparable to Blackman or Blackman-Harris window interpolating algorithm. The improved method which eliminated the spectral leakage on the second hamonic generated by the fundamental component or the interharmonics near the fundamental component and harmonics promoted the orerall analytical accuracy of the Nuttall interpolating Fourier transform with a small amount of additional computation.Wavelet transform which overcomes the faults of Fourier transform complete localization in frequency domain and no localization in time domain and can extract time-frequency information of signal is used to harmonics analysis recently. However, for the stationary harmonic signal, the analytical accuracy of wavelet transform is not high because of its serious spectral aliasing. An analytical method of haromnics and interharmonics based on recursive filter was proposed in this paper. The method which overcomes spectral aliasing of wavelet transform can accurately estimate amplitudes and phases of harmonics and interharmonics. Moreover, the recursive filter output is only decided by six previous sample values and seven previous recursive filter outputs, that computational complexity is independent of the sampling frequency.Modern spectral estimation was used to frequency estimation of harmonics and interharmonics to overcome the disadvantage of Fourier transform of low frequency resolution in short sample data. An optimal weighted Burg algorithm and a MUSIC algorithm based on propagator method (PM) were proposed. The optimal weighted Burg algorithm can reduce spectral peak excursion and eliminate spectral line splitting of Burg algorithm. The estimation of noise subspace based on PM doesn't require estimation of covariance and eigenvalue decomposition, and computational load is low. Moreover the appropriate overestimation of signal numbers will make frequency estimation performance of MUSIC based on PM be comparable to MUSIC. The frequency estimation using TLS-ESPRIT based on Multi-stage wiener filter can reduce computational burden too. The multiple simulation examples showed the proposed method had the advantages of high frequency resolution, and was applicable for harmonics and interhamonics frequency estimation of asynchronous sampling and short data length.The paper introduced Adaline neuron in the analysis of harmonics and interharmonics, complex Adaline neural network harmonic analysis model and an adaptive selecting method of frequency learning rates on enhanced Adaline neural network were proposed. The complex Adaline neural network model reduced input vectors and weights to half that of Adaline neuron. The frequency learning rates of harmonics and interharmonics were automatic adjustment according to change frequency, that could improve convergence performance and analytical precision of enhanced Adaline neural network. Several adaptive algorithms such as least mean square (LMS) Newton algorithm, variable step size LMS algorithm, recursive least square algorithm were used to Adaline neural network, that could greatly improve the convergence speed. The simulation examples demonstrated that the proposed Adaline neural network and learning algorithm achieved high precision and rapid convergence.Finally, the actual sampling data of the electrical comsuptions of computer and microwave oven, the current of ball mill and a forging plant in Hangzhou were analyzed by Root-MUSIC based on PM and Adaptive neural network method. The analysis results further verified the validity of the proposed adaptive analysis algorithm for harmonics and interharmonics, as well as obtained their parameters of harmonics (interharmonics) and electricity comsuption characteristics.