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).

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.

Conversion efficiency, power production, and cost of PV panels’ energy are remarkably impacted by external factors including temperature, wind, humidity, dust aggregation, and induction characteristics of the PV system such as tilt angle, altitude, and orientation. One of the prominent elements affecting PV panel performance and capability is dust. Nonetheless, dust features including size, shape, type, etc. are geologically known. Several mitigation methods have been studied for the reduction of dust concentration on the exterior face of the PV modules. The outcomes have demonstrated that dust concentration and pollutants remarkably affect the PV panel energy production. This paper reviews the recently developed research on the outcomes of the dust effect on PV panels in different locations and meets the needs of future research on this subject. Moreover, different cleaning methods that could be advantageous for future researchers in opting for the most applicable technique for dust removal are reviewed.

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 algorithm's 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.

Energy consumption has increased over the years and, due to the dependency on fossil energy, alternative and renewable energy sources have been integrated to address environmental concerns. However it is important to maintain the efficiency, reliability, and safety of the power grid amid the integration of different energy sources. IEEE and IEC standards regulate power quality and define thresholds for power quality events that traditionally have been detected through specialized algorithms. With machine learning it is possible to detect and classify those events using deep learning techniques that teach systems to learn by example, providing a more scalable approach to classification. Published studies in power quality with deep learning algorithms to detect disturbances rely only on simulated signals or imposed disturbances. In this paper, a deep learning neural network is trained and used to detect and classify power quality events from a database built with real electrical power grid signals measured with monitoring devices.

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.