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.

Validation of fixed speed induction generator models for inertial response using wind farm measurements

This paper outlines the use of phasor measurement unit (PMU) records to validate models of fixed speed induction generator (FSIG)-based wind farms during frequency transients. Wind turbine manufacturers usually create their own proprietary models which they can supply to power system utilities for stability studies, subject to confidentiality agreements. However, it is desirable to confirm the accuracy of supplied models with measurements from the particular installation, in order to assess their validity under real field conditions. This is prudent due to possible changes in control algorithms and design retrofits, not accurately reflected or omitted in the supplied model. One important aspect of such models, especially for smaller power systems with limited inertia, is their accuracy during system frequency transients. This paper, therefore, assesses the accuracy of FSIG models with regard to frequency stability, and hence validates a subset of the model dynamics. Such models can then be used with confidence to assess wider system stability implications. The measured and simulated response of a wind farm using doubly fed induction generator (DFIG) technology is also assessed.

The effect of feedstock cost on biofuel cost as exemplified by biomethane production from grass silage.

Abstract: The potential variance in feedstock costs can have signifi cant implications for the cost of a biofuel and the fi nancial viability of a biofuel facility. This paper employs the Grange Feed Costing Model to assess the cost of on-farm biomethane production using grass silages produced under a range of management scenarios. These costs were compared with the cost of wheat grain and sugarbeet roots for ethanol production at an industrial scale. Of the three feedstocks examined, grass silage represents the cheapest feedstock per GJ of biofuel produced. At a production cost of €27/tonne (t) feedstock (or €150/t volatile solids (VS)), the feedstock production cost of grass silage per gigajoule (GJ) of biofuel (€12.27) is lower than that of sugarbeet (€16.82) and wheat grain (€18.61). Grass biomethane is also the cheapest biofuel when grass silage is costed at the bottom quartile purchase price of silage of €19/t (€93/t VS). However, when considering the production costs (full-costing) of the three feedstocks, the total cost of grass biomethane (€32.37/GJ of biofuel; intensive 2-cut system) from a small on-farm facility ranks between that of sugarbeet (€29.62) and wheat grain ethanol (€34.31) produced in large industrial facilities. The feedstock costs for the above three biofuels represent 0.38, 0.57, and 0.54 of the total biofuel cost. The importance of feedstock cost on biofuel cost is further highlighted by the 0.43 increase in the cost of biomethane when grass silage is priced at the top quartile (€46/t or €232/t VS) compared to the bottom quartile purchase price.

RESIDUES OF THE BETA-AGONIST CLENBUTEROL IN TISSUES OF MEDICATED FARM-ANIMALS

Reports of the illegal use of clenbuterol as a growth promotant prompted the development of a competitive enzyme immunoassay for this drug. This procedure was utilized to study the elimination of clenbuterol from tissues in sheep medicated with both therapeutic and growth-promoting doses of the drug. The results indicated that prior to removal of medication clenbuterol was widely distributed throughout the animal tissues. However as the withdrawal periods increased fluid targets such as urine and bile became less effective at detecting clenbuterol usage. At both therapeutic and growth-enhancing concentrations of clenbuterol liver samples remained positive up to the maximum withdrawal time given in this experiment (15 days). Concentrations of clenbuterol likely to cause food poisoning (> 100 ng/g) were only detected in liver samples taken prior to the removal of medication. The highest recorded concentration of clenbuterol in muscle was 22.5 ng/g.