Due to its many advantages such as the improved power quality, high energy efficiency and controllability, etc. the variable speed wind turbine using a doubly fed induction generator (DFIG) is becoming a popular concept and thus the modeling of the DFIG based wind turbine becomes an interesting research topic. Fundamental frequency models have been presented but these models are often complex with significant numerical overhead as the power converter block consisting of power control, rotor side and grid side converter control and DC link are often simulated in detail. This paper develops a simple DFIG wind turbine model in which the power converter is simulated as a controlled voltage source, regulating the rotor current to meet the command of real and reactive power production. This model has the form of traditional generator model and hence is easy to integrate into the power system simulation tool such as PSS/E. As an example, the interaction between the Arklow Bank Wind Farm and the Irish National Grid was simulated using the proposed model. The model performance and accuracy was also compared with the detailed model developed by DIgSILENT. Considering the simplification adopted for the model development, the limitation and applicability of the model were also discussed in this paper.

The study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267 h of EEG data from 17 full-term newborns with seizures. The performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ~89% with one false seizure detection per hour, ~96% with two false detections per hour, or ~100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections. The results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units. The proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems.

Fault ride-through specifications listed in modern transmission and distribution grid codes specify that wind-turbine generators (WTGs) must remain connected to electricity networks at voltage levels well below nominal. Achieving reliable operation at greatly reduced voltage levels is proving problematic. A particular problem regarding power converter-based WTGs is that standard controllers designed for reliable operation around nominal voltage levels will not work as designed during low network voltages that can occur during a fault. A consequence of this is greatly increased converter currents, which may lead to converter failure. This paper presents a nonlinear controller design for a power converter-based WTG that ensures that current levels remain within design limits, even at greatly reduced voltage levels, thus enhancing the WTG's fault ride-through capability.