Red meat from animals offered a grass diet increases plasma and platelet n-3 PUFA in healthy consumers

Red meat from grass-fed animals, compared with concentrate-fed animals, contains increased concentrations of long-chain (LC) n-3 PUPA. However, the effects of red meat consumption from grass-fed animals on consumer blood concentrations of LC n-3 PUFA are unknown. The aim of the present study was to compare the effects on plasma and platelet LC n-3 PUFA status of consuming red meat produced from either grass-fed animals or concentrate-fed animals. A randomised, double-blinded, dietary intervention study was carried out for 4 weeks on healthy subjects who replaced their habitual red meat intake with three portions per week of red meat (beef and lamb) from animals offered a finishing diet of either grass or concentrate (n 20 consumers). Plasma and platelet fatty acid composition, dietary intake, blood pressure, and serum lipids and lipoproteins were analysed at baseline and post-intervention. Dietary intakes of total n-3 PUFA, as well as plasma and platelet concentrations of LC n-3 PUFA, were significantly higher in those subjects who consumed red meat from grass-fed animals compared with those who consumed red meat from concentrate-fed animals (P<0.05). No significant differences in concentrations of serum cholesterol, TAG or blood pressure were observed between groups. Consuming red meat from grass-fed animals compared with concentrate-fed animals as part of the habitual diet can significantly increase consumer plasma and platelet LC n-3 PUFA status. As a result, red meat from grass-fed animals may contribute to dietary intakes of LC n-3 PUFA in populations where red meat is habitually consumed.

Measurement-based Method for Wind Farm Power System Oscillations Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This study presents a measurement-based method for the early detection of power system oscillations, with consideration of mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet-based support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in frequency bands, whereas the SVDD method is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude, or that are resonant, can be alarmed to the system operator, to reduce the risk of system instability. The proposed method is exemplified using measured data from a chosen wind farm site.

A Wavelet-Prony Method for Modeling of Fixed-Speed Wind Farm Low-Frequency Power Pulsations

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.