Search

Diverse conditions in power systems, such as massive use of nonlinear loads, continuous switching and operation of large electrical loads, and the integration of renewable energies, among others, have adversely affected the power quality (PQ) because they produce undesirable distortions in the waveforms of voltage and current. The conventional way to quantify the PQ is using the PQ indices (PQIs). Yet, the nonstationary properties of voltage and current signals degrade the PQIs estimation whenever classical techniques are used. In this paper, a methodology based on single-sideband modulation method and the Wavelet and Hilbert transforms for the estimation of instantaneous PQIs is proposed. It is shown that the proposal yields better tracking of transitory changes in the voltage/current signals than classical techniques such as the short-time Fourier transform. The PQIs used are the root-mean-square values, frequency, total harmonic distortion, active power, reactive power, apparent power, distortion power, power factor, and total power factor. PQIs performance is validated using synthetic and real signals. [ABSTRACT FROM PUBLISHER]

Accurate and fast characterization of voltage variations helps to evaluate their severity on equipment and activate protections. In this paper, a methodology for tracking and characterization of voltage variations, sample to sample, is presented. It consists of a Hilbert transform to estimate the voltage of the signal’s envelope, a fuzzy logic system to track down the type of voltage variation, and a rule-based method for the final identification and decision making according to IEEE Std 1159-2009. Unlike some techniques presented in the literature for tracking voltage variations such as the Kalman filter and adaptive linear network techniques, the proposed methodology requires neither a harmonic model nor an algorithm to adjust the model parameters, which in many cases increases the computational burden and time tracking. It is worth mentioning that the proposed classification stage does not need a training stage; therefore, its development is easier and its efficiency does not depend on a data training set. The performance of the proposed methodology is validated and tested using synthetic signals as well as real measurements of voltage variations. In addition, an implementation of our methodology into an field-programmable gate array based system is performed in an effort to offer a low-cost and portable system-on-a-chip solution for online and real-time monitoring of voltage variations. [ABSTRACT FROM PUBLISHER]

Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively. [ABSTRACT FROM AUTHOR]

Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis. [ABSTRACT FROM AUTHOR]