David Granados-Lieberman

A Real-Time Smart Sensor for High-Resolution Frequency Estimation in Power Systems

Power quality monitoring is a theme in vogue and accurate frequency measurement of the power line is a major issue. This problem is particularly relevant for power generating systems since the generated signal must comply with restrictive standards. The novelty of this work is the development of a smart sensor for real-time high-resolution frequency measurement in accordance with international standards for power quality monitoring. The proposed smart sensor utilizes commercially available current clamp, hall-effect sensor or resistor as primary sensor. The signal processing is carried out through the chirp z-transform. Simulations and experimental results show the efficiency of the proposed smart sensor.

A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances

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 Parsevals 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.

Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

Shannon Entropy and -Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

For industry, the induction motors are essential elements in production chains. Despite the robustness of induction motors, they are susceptible to failures. The broken rotor bar (BRB) fault in induction motors has received special attention since one of its characteristics is that the motor can continue operating with apparent normality; however, at certain point the fault may cause severe damage to the motor. In this work, a methodology to detect BRBs using vibration signals is proposed. The methodology uses the Shannon entropy to quantify the amount of information provided by the vibration signals, which changes due to the presence of new frequency components associated with the fault. For automatic diagnosis, the -means cluster algorithm and a decision-making unit that looks for the nearest cluster through the Euclidian distance are applied. Unlike other reported works, the proposal can diagnose the BRB condition during startup transient and steady state regimes of operation. Additionally, the proposal is also implemented into a field programmable gate array in order to offer a low-cost and low-complex online monitoring system. The obtained results demonstrate the proposal effectiveness to diagnose half, one, and two BRBs.

Fractal dimension-based approach for detection of multiple combined faults on induction motors

Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting the process, but increasing the production costs. For this reason, a monitoring system that can efficiently diagnose the induction motor condition, even under multiple combined faults, is a demanding task. In this work, a methodology and its implementation into a field programmable gate array for an online and real-time monitoring system of multiple combined faults are presented. First, the fractal dimension approach, using the Katz algorithm, is introduced as a measure of variation of 3-axis startup vibration signals for the induction motor condition, considering that these signals describe changes on its dynamic characteristics due to the different faults. Then, an artificial neural network determines in an automatic way the induction motor condition according to the fractal dimension values. The obtained results show a higher overall efficiency than previous works for detecting broken rotor bars, outer-race bearing defects, unbalance, and their combinations, as well as a healthy condition.

Reconfigurable instrument for power quality monitoring in 3-phase power systems

From voltage and current signals it is possible to obtain relevant information regarding 3-phase power supply features like signal amplitude, frequency, harmonic contents, phase, among others. This information is useful to solve problems like power quality monitoring, diagnosis of electrical machines, electric systems protection, and control. This work presents preliminary results on the design of a reconfigurable tool for electric parameter monitoring in 3-phase power systems based on low-cost field programmable gate array technology, which implements different algorithms depending on the application. Obtained results from two study cases show the feasibility of utilizing the proposed instrument for monitoring power quality indexes in 3-phase power systems.

Instantaneous Power Quality Indices Based on Single-Sideband Modulation and Wavelet Packet-Hilbert Transform

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.

A New Methodology for Tracking and Instantaneous Characterization of Voltage Variations

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 signals 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.

Tracking of voltage variations by means of an adaptive filter and fuzzy logic

Monitoring of voltage variations is a demanding issue for academic and industrial fields due mainly to their negative impact on equipment. In this work, a methodology based on adaptive filter using the least mean squares algorithm for tracking of voltage variations and a fuzzy logic system for automatic classification are proposed. The proposal consists of three stages: 1) denoising through a lowpass filter to remove non-fundamental frequency components, 2) envelope and type of voltage tracking, and 3) final classification according to the IEEE Std. 1159 using a rule-based decision process. In order to validate and test the proposal, a set of synthetic and real signals is used. The obtained results demonstrate the proposal effectiveness to detect and classify voltage variations, even when they are embedded in high level noise. Unlike other reported works, the proposed fuzzy logic system allows the tracking of the voltage variation such as sag, swell, or interruption over time, it means sample to sample.

The application of EMD methods to power quality signals

Over the past few years, power quality (PQ) monitoring has become an important topic because of the negative impact of different machines to the electrical network and to the susceptibility of critical equipment. There are different disturbances that affect the PQ; therefore, in order to apply a proper solution, these have to be correctly detected and classified. In general, signal processing techniques are applied for their detection. Recently, several approaches based on empirical mode decomposition (EMD) method have been reported; however, the selection of the best-suited method in terms of processing and performance for a particular case can be a complicated decision-making process. In this paper, a quantitative and qualitative comparative study using EMD methods such as conventional EMD, ensemble EMD, and complete ensemble EMD is presented. The study is applied to synthetic and real PQ signals, in which aspects of the computational cost and decomposition accuracy are discussed.