In this paper a geostatistic wind direction model is applied to trace a wind speed map, based on data from official measurement weather stations distributed within the region of Andalucia-Spain. Each station's performance is assessed by comparing real measurements to those resulting from the linear interpolation of the rest. Once an error is associated to the station, the error is drawn in a map, in which minimum error zones can be delimited. Frequency and wind speed in each direction are the magnitudes of interest to get a first categorization of wind resources associated to the region. The interest of the method relies in the possibility of forecasting everywhere within the region with an error inside the tolerable margins.
This work accomplishes the classification of Power Quality (PQ) disturbances using fourth-order sliding cumulants' maxima as the key feature. These statistics estimators are calculated over high-pass filtered real-life signals, to avoid the low-frequency 50-Hz sinusoid. Four types of electrical AC supply anomalies constitute the starting grid of a competitive layer performance, which manages to classify 90 signals within a 2D-space (whose coordinates are the minima and the maxima of the sliding cumulants calculated over each register). Four clusters have been clearly identified via the competitive network, each of which corresponds to a type of anomaly. Then, a Self-Organizing Network is conceived in order to guess additional classes in the feature space. Results suggest the idea of two additional sets of signals, which are more related to the degree of signals' degeneration than to real new groups of anomalies. We collaterally conclude the need of additional features to face the problem of subclass division. The experience sets the foundations of an automatic procedure for PQ event classification.
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