Original scientific paper Accurate estimation of harmonic parameter is an important task in signal processing of power system. A new class of flat-top windows is proposed, which is generated by self-convolutions of the fast-decaying minimum-sidelobe flat-top (FDMS-FT) window in the time-domain. The mainlobe and sidelobe features of the new window are studied. In addition, to improve the flatness of mainlobe of the new window, the coefficients of its parent window are optimized. A window-length changeable discrete phase difference correction algorithm based on the new window is presented to estimate power harmonic parameter. In order to inspect the efficiency and accuracy of the presented method, several computer simulations and practical experiments were conducted with power multi-frequency signals. Results show that the proposed method can reduce the computation load efficiently and gives a high parameter estimation accuracy of power harmonic.Keywords: fast Fourier transform; flat-top window; parameter estimation; power system harmonic; spectral correction; spectral leakage Spektralna korekcijska metoda utemeljena na poboljšanom spiralnom podesivom prozoru za procjenu parametara harmonijske snage Izvorni znanstveni članak Točna procjena harmonijskog parametra je važan zadatak u obradi signala elektroenergetskog sustava. Predlaže se nova vrsta flat-top prozora koja se generira vlastitim konvolucijama brzo padajućeg flat-top prozora (FDMS-FT) u domenu vremena. Proučavaju se značajke bočnog i glavnog režnja novog prozora. Nadalje, kako bi se poboljšala glatkoća glavnog režnja novog prozora, optimiziraju se koeficijenti matičnog prozora. Predstavlja se izmjenjivi algoritam ispravke razlike faze duljine prozora baziran na novom prozoru kako bi se procijenio strujni harmonični parametar. Kako bi se provjerila učinkovitost i točnost prikazane metode, provedeno je nekoliko računalnih simulacija i praktičnih eksperimenata s višefrekvencijskim strujnim signalima. Rezultati pokazuju da predložena metoda može učinkovito smanjiti opseg računanja i daje visok parametar točnosti procjene harmonijske snage.

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Accurate harmonic emission level evaluation is the basis of distinguishing the responsibility of harmonic pollution between power supply system and consumer. A new harmonic emission level evaluation method via principal component regression (PCR) is proposed in this paper. Firstly, the principle of PCR is analyzed. Then, harmonic impedance of supply system is evaluated by PCR-based method. Subsequently, harmonic emission level of supply system and consumer is evaluated according to the harmonic impedance of supply system. This technique has the advantage of high accuracy in estimating harmonic emission level. The effectiveness of the proposed method was verified by computer simulations and field test.

The stability and consistency of harmonic amplitude measurement is very important for power quality analysis and assessment. Some random factors may influence the measurement results in the real world, for example the initial sampling time. In this paper, we put this view a little forward, and prove that the amplitude errors with DFT method change periodically with the initial-sampling time, and DFT method “may” give relatively worse results, with the window which has better characteristics. To mitigate this random influence and to achieve more stable measurement precision, the well known shifting window average DFT, which is based on expectation calculation, is adopted. The effectiveness of this method and its recursive mode has been tested on several simulated signals and measured signals generated by Electrical Power Standards Fluke 6100A.

Kraft pulp and wood powder rom Acacia Spp. were selected for the development of rapid, minimally-destructive, and predictions of kappa number and pulp yield, by means of near infrared reflectance (NIR) spectra. The models, based on Partial Least Squares Regression (PLS-R), were established with fifty-four calibration samples selected by Principle Component Analysis (PCA), while the validation models resulted from nineteen samples that were not included in the calibration set. The and stability of calibration models were evaluated by coefficient of determination for calibration (R2cal) and root mean square error of cross-validation (RMSECV). The coefficient of determination for validation (R2val) and root mean square error of prediction (RMSEP) were used for validation models. The main results showed that: (1) the predictive models from pulp were more credible in terms of the R2cal and R2val values than those from wood powder by 25 to 70%; and (2) a validation model for kappa number from pulp showed a better stability than the corresponding calibration model, since RMSEP was 23.5% less than RMSECV, while calibration models for pulp yield were more steady than validation models. This study provided reliable models for predicting kappa number and pulp yield rapidly and with a minimal need for physical sampling.