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In wind farms, individual turbines disturb the wind field by generating wakes that influence other turbines in the farm. From a control point of view, there is an interest in dynamic optimization of the balance between fatigue and production, and an understanding of the relationship between turbines manifested through the wind field is hence required. This paper develops models for this relationship. The result is based on two new contributions: the first is related to the estimation of effective wind speeds, which serves as a basis for the second contribution to wind speed prediction models. Based on standard turbine measurements such as rotor speed and power produced, an effective wind speed, which represents the wind field averaged over the rotor disc, is derived. The effective wind speed estimator is based on a continuous–discrete extended Kalman filter that takes advantage of nonlinear time varying turbulence models. The estimator includes a nonlinear time varying wind speed model, which compared with literature results in an adaptive filter. Given the estimated effective wind speed, it is possible to establish wind speed prediction models by system identification. As the prediction models are based on the result related to effective wind speed, it is possible to predict wind speeds at neighboring turbines, with a separation of over 700 m, up to 1 min ahead reducing the error by 30% compared with a persistence method. The methodological results are demonstrated on data from an off‐shore wind farm. Copyright © 2011 John Wiley & Sons, Ltd.

Cascaded connection of power converters is a dominant connection form in DC microgrids. In such systems, despite the possible instability caused by the impedance interactions between the individually designed converters, tightly regulated load converters acting as constant power loads (CPLs) tend to destabilize the system owing to their negative resistance characteristics. Hence, this paper proposes a new virtual series RC damper in parallel with the source-side converter's capacitor without compromising the load's dynamic performance. Using this design-oriented active damping method, which utilizes a simple control structure with a more straightforward tuning of the control parameter, the stability and performance of the system are guaranteed. The feasibility and robustness of the suggested active stabilization idea against unanticipated variations in input voltage amplitude, and CPL power rating (load changes) as well as step changes in output voltage reference, are also authenticated. The control and operation principles, as well as the circuit physical meaning realized by the presented technique for three cascaded systems comprising the basic DC/DC converters feeding CPLs, are theoretically analyzed. Simulation and experimental results are provided to validate the effectiveness of the proposed active stabilizer.

With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.Index Terms-Energy yields, levelized production cost (LPC), optimized placement, particle swarm optimization (PSO), wake effect, wake model.