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For the development of the future smart grid, the detection of power quality events is a key issue for the power system monitoring. Voltage sags, swells, harmonics (variations) and interruptions, which produce large losses for commercial and industrial consumers, are the main events to be considered due to the sensitivity of equipment to these electrical anomalies. The steady-state events are even more frequently accompanied by transients, the discrimination and localization being far more exigent and requiring advanced signal separating tools to be incorporated in the measurement equipment. This paper shows the event detection performance of the spectral kurtosis as a signal separating tool in the frequency domain. The disturbances under test are hybrid signals resulting from the coupling between amplitude defects and non-desired higher frequencies. Being a fourth-order spectrum, the kurtosis is confirmed as a noise-resistant tool that enhances impulsiveness, therefore characterizing the electrical anomalies. In the beginning of the analysis, the voltage sag is established as a reference; then, the disturbances (oscillatory transients and harmonics) are coupled at the starting and ending instants of the sag, resulting in complex hybrid events. The results show that the spectral kurtosis enhances the detection of both types of events (steady state and transients), which are outlined in a bump shape in the fourth-order frequency pattern and centered in the main carrier frequency. Indeed, while the oscillatory transients are associated with softer and lower-amplitude peaks, the harmonics correspond to crisper and higher ones. As these mixed electrical faults are very common in the actual power grid, the article postulates the higher-order spectra to be implemented in prospective online measurement instruments. [ABSTRACT FROM AUTHOR]

Different forecasting methodologies, classified into parametric and nonparametric, were studied in order to predict the average concentration of P M 10 over the course of 24 h. The comparison of the forecasting models was based on four quality indexes (Pearson's correlation coefficient, the index of agreement, the mean absolute error, and the root mean squared error). The proposed experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras (Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained with the reference models through the introduction of low-quality measurements as exogenous information. This proves that it is possible to improve performance by using additional information from the existing nonlinear relationships between the concentration of the pollutants and the meteorological variables. [ABSTRACT FROM AUTHOR]

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m 2 and a standard deviation of 36.1 W/m 2 . The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids. [ABSTRACT FROM AUTHOR]