Fernando M. Janeiro

PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System

This paper presents a system for detection and classification of power quality (PQ) voltage disturbances. The proposed system applies the following methods to detect and classify PQ disturbances: digital filtering and mathematical morphology are used to detect and classify transients and waveform distortions, whereas for short- and long-duration disturbances (such as sags, swells, and interruptions), the analysis of the root-mean-square (RMS) value of the voltage is employed. The proposed combined approach identifies the type of disturbance and its parameters such as time localization, duration, and magnitude. The proposed system is suitable for real-time monitoring of the power system and implementation on a digital signal processor (DSP).

Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

The estimation of the parameters of defects from eddy current nondestructive testing data is an important tool to evaluate the structural integrity of critical metallic parts. In recent years, several works have reported the use of artificial neural networks (ANNs) to deal with the complex relation between the testing data and the defect properties. To extract relevant features used by the ANN, principal component analysis, wavelet decomposition, and the discrete Fourier transform have been proposed. In this paper, a method to estimate dimensional parameters from eddy current testing data is reported. Feature extraction is based on the modeling of the testing data by a template of additive Gaussian functions and nonlinear regressions to estimate their parameters. An ANN was trained using features extracted from a synthetic data set obtained with finite-element modeling of the eddy current probe. The proposed method was applied to both simulated and measured data, providing good estimates.

Recent Developments on Impedance Measurements With DSP-Based Ellipse-Fitting Algorithms

In this paper, recent advances of a new digital-signal-processor (DSP)-based impedance measurement instrument under development are presented. The digital signal processing algorithms are based on ellipse fitting for the extraction of the acquired sine signal parameters. so that the impedance magnitude and phase can be determined. Special attention is devoted to the improvement of the algorithms efficiency, i.e., by enabling the acquisition of a large number of samples by processing nonconsecutive data segments with no extra memory requirements. This capability is shown to reduce the experimental uncertainties of the estimated parameters. The systematic errors caused by the two different acquisition channels are measured and taken into account. The combined experimental measurement uncertainty is evaluated for the frequency sweep measurement of a particular impedance.

Impedance Measurements Using Genetic Algorithms and Multiharmonic Signals

In this paper, a procedure to measure impedances using data-acquisition boards and genetic algorithms is developed. This approach to impedance measurements has the advantage of being low cost. The multiharmonic acquired waveforms are characterized using a genetic algorithm that finds the frequency of the signal, which, in turn, is used in a multiple linear least-squares (LS) waveform-fitting algorithm. The magnitude and phase of the unknown impedance can then be evaluated. A multiharmonic signal is used so that the frequency dependence of the impedance can be obtained from a single measurement. The measurement results are validated by measurements made with an impedance analyzer. The main advantage of the genetic algorithm over traditional search methods is its robustness to convergence problems.

Gene Expression Programming in Sensor Characterization: Numerical Results and Experimental Validation

In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterization. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterization are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor.

Impedance spectroscopy of a vibrating wire for viscosity measurements

Many routine viscosity measurement systems suffer from low accuracy and some are also operator dependant which creates repeatability problems. This paper describes the ongoing developments of a viscosity measurement system based on a vibrating wire cell. The proposed improvements include the simultaneous fitting of the magnitude and phase of the measured impedance and the test of different equivalent impedance circuits. Since the fitting function has a high number of search variables and contains many local minima, a hybrid method involving genetic algorithms as well as a traditional search algorithm is used. The system is tested with liquid diisodecyl phthalate (DIDP) and the viscosity results are compared to reference values available in the literature. A new equivalent impedance circuit is also suggested as one of the possible circuits to model the cell behavior in the future, when used with ionic liquids.

On-line Detection and Classification of Power Quality Disturbances in a Single-phase Power System

A new method for detection and classification of power quality (PQ) disturbances in single-phase power systems is presented in this paper. The method detects transients, waveform distortions and short and long duration disturbances such as sags or swells. The method was developed with special consideration for its suitability for implementation in a digital signal processor (DSP) and for real-time operation. The proposed method was implemented and its performance was verified on detection of real PQ disturbances in a PC based system monitoring a single-phase power system.

Uncertainty Analysis of Impedance Measurements Using DSP Implemented Ellipse Fitting Algorithms

Impedance measurements are extremely important in many fields of science and accurate impedance measuring devices are therefore required. Besides accuracy, low-cost and portability are also sought after characteristics in some applications. A DSP based prototype has been developed to accurately measure impedances. An ellipse fitting algorithm is implemented in this device introducing many advantages in terms of speed and memory requirements, when compared to other signal processing algorithms commonly used, such as sine-fitting. In this paper, the developed prototype is used to measure an RLC series impedance in a range of frequencies and the experimental measurement uncertainty is analyzed.

An efficient approach to detect and classify power quality disturbances

Purpose – The purpose of this paper is to develop a new method for detection and classification of power quality disturbances such as transients, waveform distortions, sags, swells and interruptions.Design/methodology/approach – For the purposes of the proposed method, the power quality disturbances are divided into two groups. Different algorithms are applied to detect and classify the disturbances from each of the two groups. For the processing of transients and waveform distortions, digital high‐pass filter and the mathematical morphology closing are used. Calculation of the RMS value is used for detection of sags, swells and interruptions.Findings – The proposed method was implemented in a PC‐based measuring setup. The measuring setup was used in a seven‐months‐long monitoring of a single‐phase power system. In the course of the monitoring, the proposed method was verified on over 19,000 transients, 3,500 waveform distortions, 77 sags and 18 interruptions.Research limitations/implications – The classi...

Sensor characterization using gene expression programming evolutionary algorithms

In this paper, sensor characterization is done based on gene expression programming evolutionary algorithms. The process presented is suited for situations where there is no knowledge of the sensor equivalent circuit and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine the best suited equivalent circuit and choose the circuit component that better describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses.