Development of automatic intelligent system for on-line voltage security control of power systems

The majority of recent large-scale blackouts have been caused by voltage instability. A prompt on-line assessment of voltage stability for preventive corrective control of electric power systems is one of the key objectives for Control centers. The use of classical approximation methods alone is complicated. Therefore, several modified methods combined with machine learning algorithms enabling security assessment in real time have been proposed over the last years. The paper presents an automatic intelligent system for on-line voltage security control, which is based on the model of decision trees Proximity Driven Streaming Random Forest (PDSRF). In this case, the combination of original properties of PDSRF and capabilities of L-index as a target vector makes it possible to provide the functions of dispatcher warning, localization of critical nodes, and ensure direct interaction with the security automation systems. The efficiency of the proposed system was demonstrated using various test schemes of IEEE

Identification of extreme wind events using a weather type classification

ABSTRACT: The identification of extreme wind events and their driving forces are crucial to better integrating wind generation into the power system. Recent work related the occurrence of extreme wind events with some weather circulation patterns, enabling the identification of (i) wind power ramps and (ii) low-generation events as well as their intrinsic features, such as the intensity and time duration. Using Portugal as a case study, this work focuses on the application of a weather classification-type methodology to link the weather conditions with wind power generation, namely, the different types of extreme events. A long-term period is used to assess and characterize the changes in the occurrence of extreme weather events and corresponding intensity on wind power production. High variability is expected under cyclonic regimes, whereas low-generation events are most common in anticyclonic regimes. The results of the work provide significant insights regarding wind power production in Portugal, enabling an increase in its predictability.info:eu-repo/semantics/publishedVersio