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Bristol, United Kingdom

Arulampalam A.,University of Peradeniya | Ramtharan G.,Garrad Hassan and Partners Ltd | Ekanayake J.B.,University of Cardiff | Tennakoon A.P.,Ceylon Electricity Board | And 2 more authors.
2010 5th International Conference on Industrial and Information Systems, ICIIS 2010 | Year: 2010

The electromechanical transients during a deloading of a DFIG turbine and the Fault Ride Through (FRT) capability of a DFIG wind farm connected through HVDC transmission lines are discussed. The electromechanical oscillations during a deloading operation of a DFIG wind turbine generator are simulated using BLADED software. Then power reduction control during a fault was achieved by reducing the power from the wind farm as a whole and by deloading the individual wind generator. A new power blocking technique applied at the offshore converter station was used to reduce the wind farm power output. Simultaneous control of the wind farm and wind turbine power outputs enabled a smooth power reduction during the fault. ©2010 IEEE. Source


Caliao N.D.,Mindanao University of Science and Technology | Ramtharan G.,Garrad Hassan and Partners Ltd | Ekanayake J.,University of Cardiff | Jenkins N.,University of Cardiff
Proceedings of the Universities Power Engineering Conference | Year: 2010

A power oscillation damping controller for fully rated converter wind turbines was investigated. Small signal analysis and time domain simulations were used to investigate the performance of the fully rated converter wind turbine with a power oscillation damping controller when connected to a single bus representation of the large power system. A power oscillation damping controller for fully rated converter wind turbines was shown to improve network damping. Simulation results also show that, with a power oscillation damping controller incorporated into the grid side converter of fully rated converter wind turbines, decoupling between the generator and grid is maintained. Source


Cruz J.,Garrad Hassan Iberica SLU | Sykes R.,Garrad Hassan and Partners Ltd | Siddorn P.,University of Oxford | Taylor R.E.,University of Oxford
IET Renewable Power Generation | Year: 2010

Estimates regarding the assessment of the energy absorption characteristics of an array of wave energy converters (also referred to as a wave farm) are presented. Regular and irregular waves are used as input in a frequency-domain hydrodynamic model, which allows iterations in the array layout and farm control strategy. Under such an approach each array element can be controlled independently while keeping the design objective (maximisation of the wave farm energy yield). The distribution of power take-off (PTO) loading on the various array elements, as induced by the incoming sea, is also investigated. The approach is verified by comparing the estimates with results from a semi-analytic method developed at the University of Oxford. The overall objective of the study is to quantify the influence of the array layout and farm control in the performance of a wave farm under the action of irregular waves. The results show that the energy yield and the PTO loads are affected by such factors; hence these can be seen as key design drivers in order to reduce the uncertainty and thus the cost of energy when planning a wave farm. Further studies may address additional constraints, either technical or economical. This study is expected to contribute to the development of specific modules of a design optimisation tool for wave farms, and extends the findings originally presented at the Eighth European Wave and Tidal Energy Conference. © 2010 © The Institution of Engineering and Technology. Source


Manning J.,University of Surrey | Hancock P.,University of Surrey | Whiting R.,Garrad Hassan and Partners Ltd
Wind Engineering | Year: 2010

Comparisons are made between Meteodyn WT (MWT) and wind tunnel measurements from the three RUSHIL test cases that represent hills of increasing steepness. The two steeper cases are of interest; in one the flow on the lee side is close to separation (Hill 5), for the other the flow has clearly separated (Hill 3). Although it is well known that WAsP 8.3 (WP) cannot predict separation, its predictions are included to represent the current industry standard. Both models agree well with mean wind speeds measured upstream of the Hill 5 crest. MWT gives significantly better but not good agreement upstream of the Hill 3 crest, where WP significantly over-predicts the speed-up. Downstream, MWT predictions are closer to measurements, but predict a smaller separation bubble on Hill 3, due to limitations of the turbulence model. A practical viewpoint requires improved modelling to have only a minimal impact on computational resource requirements. Source


L. Mackay E.B.,Garrad Hassan and Partners Ltd | Challenor P.G.,UK National Oceanography Center | Bahaj A.S.,University of Southampton
Ocean Engineering | Year: 2010

Extreme value theory is commonly used in offshore engineering to estimate extreme significant wave height. To justify the use of extreme value models it is of critical importance either to verify that the assumptions made by the models are satisfied by the data or to examine the effect violating model assumptions. An important assumption made in the derivation of extreme value models is that the data come from a stationary distribution. The distribution of significant wave height varies with both the direction of origin of a storm and the season it occurs in, violating the assumption of a stationary distribution. Extreme value models can be applied to analyse the data in discrete seasons or directional sectors over which the distribution can be considered approximately stationary. Previous studies have suggested that models which ignore seasonality or directionality are less accurate and will underestimate extremes. This study shows that in fact the opposite is true. Using realistic case studies, it is shown that estimates of extremes from non-seasonal models have a lower bias and variance than estimates from discrete seasonal models and that estimates from discrete seasonal models tend to be biased high. The results are also applicable to discrete directional models. © 2010 Elsevier Ltd. All rights reserved. Source

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