In this paper we introduce a self-tuning Kalman filter for fast time-domain amplitude estimation of noisy harmonic signals with non-stationary amplitude and harmonic distortion, which is the problem of a contactvoltage measurement to which we apply the proposed method. The research method is based on the self-tuning of the Kalman filter's dropping-off behavior. The optimal performance (in terms of accuracy and fast response) is achieved by detecting the jump of the amplitude based on statistical tests of the innovation vector of the Kalman filter and reacting to this jump by adjusting the values of the covariance matrix of the state vector. The method's optimal configuration of the parameters was chosen using a statistical power analysis. Experimental results show that the proposed method outperforms competing methods in terms of speed and accuracy of the jump detection and amplitude estimation.
The quality of the supplied power by electricity utilities is regulated and of concern to the end user. Power quality disturbances include interruptions, sags, swells, transients and harmonic distortion. The instruments used to measure these disturbances have to satisfy minimum requirements set by international standards. In this paper, an analysis of multi-harmonic least-squares fitting algorithms applied to total harmonic distortion (THD) estimation is presented. The results from the different least-squares algorithms are compared with the results from the discrete Fourier transform (DFT) algorithm. The algorithms are assessed in the different testing states required by the standards.
A limited ability to discriminate between different materials is the fundamental problem with all conventional eddy-current-based metal detectors. This paper presents the use, evaluation and classification of nontraditional excitation signals for eddy-current metal detectors to improve their detection and discrimination ability. The presented multi-frequency excitation signals are as follows: a step sweep sine wave, a linear frequency sweep and sin(x)/x signals. All signals are evaluated in the frequency domain. Amplitude and phase spectra and polar graphs of the detector output signal are used for classification and discrimination of the tested objects. Four different classifiers are presented. The classification results obtained with the use of poly-harmonic signals are compared with those obtained with a classical single-tone method. Multifrequency signals provide more detailed information, due to the response function – the frequency characteristic of a detected object, than standard single-tone methods. Based on the measurements and analysis, a metal object can be better distinguished than when using a single-tone method.
This paper presents a new modification of the least-squares Prony’s method with reduced sampling, which allows for a significant reduction in the number of the analysed signal samples collected per unit time. The specific combination of non-uniform sampling with Prony’s method enables sampling of the analysed signals at virtually any average frequency, regardless of the Nyquist frequency, maintaining high accuracy in parameter estimation of sinusoidal signal components. This property allows using the method in measuring devices, such as for electric power quality testing equipped with low power signal processors, which in turn contributes to reducing complexity of these devices. This paper presents research on a method for selecting a sampling frequency and an analysis window length for the presented method, which provide maximum estimation accuracy for Prony’s model component parameters. This paper presents simulation tests performed in terms of the proposed method application for analysis of harmonics and interharmonics in electric power signals. Furthermore, the paper provides sensitivity analysis of the method, in terms of common interferences occurring in the actual measurement systems.