دانلود رایگان مقاله انگلیسی مدلسازی و کنترل توربین بادی و مزرعه - الزویر 2018

Abstract

The Matlab/Simulink model of the Supergen (Sustainable Power Generation and Supply) Wind 5 MW exemplar wind turbine, which has been employed by a number of researchers at various institutions and Universities over the last decade, is reported. It is subsequently improved, especially in speed, to facilitate wind farm modelling, which usually involves duplicating wind turbine models. The improvement is achieved through various stages, including prewarping, discretisation using Heun’s method in addition to Euler method, and conversion to C. Results are presented to demonstrate that improvement in speed is significant and that the resulting wind turbine model can be used for wind farm modelling more efficiently. It is important to highlight that improvement in speed is achieved without compromising the complexity of the turbine model; that is, each turbine included in a wind farm is neither simplified nor compromised. The use of the wind farm model for testing a wind farm controller that has recently been introduced is also demonstrated.

Conclusions

The Matlab model of the Supergen Wind 5 MW exemplar wind turbine, which has been used by various researchers over the last decade, especially within the Supergen Wind Consortium is improved in speed. This is achieved through prewarping, discretisation and conversion to C in order. Discretising optimally prevents the Simulink solver from non-optimally performing numerical integration for simulating the stiff drive-train module, and thus improves the simulation speed. Discretisation is also an essential prerequisite for conversion to C. Subsequently converting the discretised model to C, and then to CMEX for simulation in Matlab/SIMULINK, further improves the simulation speed. It allows the discretised model to be duplicated and extended to constitute a wind farm model without increasing the simulation time exponentially in contrast to the original continuous model. The simulation results demonstrate that even with one hundred turbine models included in a wind farm, the simulation speed remains at 70s. It is important again to emphasise that each turbine model is neither simplified nor compromised. In the second part of the paper, the wind farm controller introduced in [11] is tested by application to a wind farm model of 30 turbine models, which is only possible because the turbine model has been improved in speed – in [11], the available wind farm model could only contain 10 turbines due to the computational effort and speed required for simulating each turbine model. The simulation results demonstrate that the wind farm controller performs satisfactorily when applied to the wind farm model of 30 turbines. The comparison of the simulation results to those from the situations in which the wind farm controller is applied to smaller wind farm models demonstrates that the fluctuation of the wind farm power output decreases as the wind farm size increases. The controller is therefore scalable and could be more valuable to larger wind farms.