T. Lobos

Real-time determination of power system frequency

The main frequency is an important parameter of an electrical power system. The frequency can change over a small range due to generation-load mismatches. Some power system protection and control applications, e,g, frequency relay for load shedding, load-frequency controller, require accurate and fast estimation of the frequency. Most digital algorithms for measuring frequency have acceptable accuracy if voltage waveforms are not distorted. However, due to nonlinear devices, e,g, semiconductor rectifiers, electric arc furnaces, the voltage waveforms can include higher harmonics. The paper presents a new method of measurement of power system frequency, based on digital filtering and Pronys estimation method. Simulation results confirm, that the proposed method is more accurate than others, e,g, than the method based on the measurement of angular velocity of the rotating voltage phasor.

Automated classification of power-quality disturbances using SVM and RBF networks

The authors propose a new method of power-quality classification using support vector machine (SVM) neural networks. Classifiers based on radial basis function (RBF) networks was, in parallel, applied to enable proper performance comparison. Both RBF and SVM networks are introduced and are considered to be an appropriate tool for classification problems. Space phasor is used for feature extraction from three-phase signals to build distinguished patterns for classifiers. In order to create training and testing vectors, different disturbance classes were simulated (e.g., sags, voltage fluctuations, transients) in Matlab. Finally, the investigation results of the novel approach are shown and interpreted.

Measurement of IEC Groups and Subgroups using Advanced Spectrum Estimation Methods

The International Electrotechnical Commission (IEC) standards characterize the waveform distortions in power systems with the amplitudes of harmonic and interharmonic groups and subgroups. These groups/subgroups utilize the waveform spectral components obtained from a fixed frequency-resolution discrete Fourier transform (DFT). Using the IEC standards allows for a compromise among the different goals, such as the needs for accuracy, simplification, and unification. In some cases, however, the power-system waveforms are characterized by spectral components that the DFT cannot capture with enough accuracy due to the fixed frequency resolution and/or the spectral leakage phenomenon. This paper investigates the possibility of a group/subgroup evaluation using the following advanced spectrum estimation methods: adaptive Prony, estimation of signal parameters via rotational invariance techniques, and root multiple-signal classification (MUSIC). These adaptive methods use variable lengths of time windows of analysis to ensure the best fit of the waveforms; they are not characterized by the fixed frequency resolution and do not suffer from the spectral leakage phenomenon. This paper also presents the results of the applications of these methods to three test waveforms, to current and voltage waveforms obtained from simulations of a real DC arc-furnace plant, and to waveforms measured at the point of common coupling of the low-voltage network supplying a high-performance laser printer.

Analysis of power system transients using wavelets and Prony method

Transients resulting from switching of capacitor banks in electrical distribution systems affects power quality. The transient overvoltages can theoretically reach peak phase to earth values in the order of 2.0 p.u. High current transients can reach values to ten times the capacitor nominal current with duration of several milliseconds. Another severe operating condition is the switching on a second capacitor bank connected to the same bus. In the work, the characteristics of the transients are analyzed. The time of the beginning of a transient process is detected using a wavelet transform. The frequencies of transient components have been investigated applying the Fourier technique and the Prony model. The investigations show the advantages of the methods basing on the Prony model, over the Fourier technique. A distribution system was simulated using the EMTP software.

Parameter estimation of distorted signals using Prony method

Modern frequency power converters generate a wide spectrum of harmonic components. Large converter systems can also generate noncharacteristic harmonics and interharmonics. Standard tools of harmonic analysis based on the Fourier transform assume that only harmonics are present and the periodicity intervals are fixed, while periodicity intervals in the presence of interharmonics are variable and very long. In the case of frequency converters the periods of the main component are unknown. The Prony method as applied for signal analysis in power frequency converter was tested in the paper. The method does not show the disadvantages of the traditional tools and allow exact estimation the frequencies of all or dominant components, even when the periodicity intervals are unknown. To investigate the appropriateness of the method several experiments were performed. For comparison, similar experiments were repeated using the FFT. The comparison proved the superiority of the Prony method.

Application of higher‐order spectra for signal processing in electrical power engineering

During recent years, higher order statistics (HOS) have found a wide applicability in many diverse fields, e.g. biomedicine, harmonic retrieval and adaptive filtering. In power spectrum estimation, the signal under consideration is processed in such a way that the distribution of power among its frequency is estimated and phase relations between the frequency components are suppressed. Higher order statistics and their associated Fourier transforms reveal not only amplitude information about a signal, but also phase information. If a non‐Gaussian signal is received along with additive Gaussian noise, a transformation to higher order cumulant domain eliminates the noise. These are some methods for estimation of signal components, based on HOS. In the paper we apply the MUSIC method both for the correlation and the fourth order cumulant, to investigate the state of asynchronous running of synchronous machines and the fault operation of inverter‐fed induction motors. When the investigated signal is distorted...

Computation of voltage sag initiation with Fourier based algorithm, Kalman filter and Wavelets

Dynamic Voltage Restorers (DVR) have been successfully applied for voltage dip mitigation in the last years. Especially in systems with nonlinear loads and wind turbine generation DVR units support the Power Quality enhancement. The reliability and quality of DVR operation depends mostly on fast and accurate voltage dip detection. Detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation using carefully selected advanced signal processing methods such as Fourier based algorithm, Kalman filtering and Wavelets. Additionally, the traditional and common technique of RMS value tracking has been mentioned. The algorithms have been tested under different conditions: voltage dip with phase jump, noise, frequency variations.

Time–Frequency Analysis of Complex Space Phasor in Power Electronics

This paper considers applying nonparametric and parametric methods for the calculation of the time-frequency representation of nonstationary signals in power electronics. A space phasor is proposed as a complex representation of a three-phase signal to calculate the spectrum of positive-and negative-sequence components. The developed methods are tested with nonstationary multiple-component signals occurring during the fault operation of inverter-fed drives. First, the main differences between parametric and nonparametric methods are underlined. Then, some aspects of additional kernel functions are introduced on the basis of Wigner and Choi-Williams distributions. An additional comparison of uncertainty of measurements using the described methods, as well as a widely used Fourier technique, can be useful for practitioners. The proposed methods allow tracking instantaneous frequency as well as magnitude that can lead to applications in the diagnosis and power quality areas.

Analysis of non‐stationary electric signals using the S‐transform

Purpose – The purpose of this paper is to compare the accuracy of tracking the amplitude and frequency changes of non‐stationary electric signals.Design/methodology/approach – Short‐time fourier transform (STFT) and S‐transform algorithms were applied to analyze non‐stationary signals originating from switching of capacitor banks in a power system.Findings – The S‐transform showed possibilities of sharp localization of the basic component, and allowed improvement of tracking dynamism the transient components in comparison to STFT.Practical implications – S‐transform is a better tool for the analysis of non‐stationary waveforms in power systems and its properties can be used for diagnostic and power quality applications.Originality/value – The dynamic tracking of the changes in time and frequency of real‐like signals originating from a power system are investigated in this paper.

Application of advanced signal processing methods for accurate detection of voltage dips

Custom Power Devices like the dynamic voltage restorer have been applied for voltage dip mitigation in the last years. These electronic equipments need fast and reliable voltage dip detection algorithms. Such detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation by using different advanced signal processing methods such as Kalman filtering, Fourier based algorithms and Wavelet processing. The three algorithms have been tested under different conditions: voltage dip with phase jump, noise, frequency variations. The final implementation on a Texas Instrument DSP TMS320F2812DSP has been done.