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A dynamic model for the wind flow in wind farms is developed in this paper. The model is based on the spatial discretization of the linearized Navier–Stokes equation combined with the vortex cylinder theory. The spatial discretization of the model is performed using the finite difference method, which provides the state-space form of the dynamic wind farm model. The model provides an approximation of the behavior of the flow in the wind farm and obtains the wind speed in the vicinity of each wind turbine. Afterwards, the model is validated using measurement data of Energy research Center of the Netherlands’ Wind turbine Test site in Wieringermeer in the Netherlands and by employing the outcomes of two other wind flow models. The end goal of this work is to present the wind farm flow model by ordinary differential equations, to be applied in wind farm control algorithms along with load and power optimizations. Copyright © 2013 John Wiley & Sons, Ltd.

The aim of this paper is to develop a controller for wind farms to optimize the load and power distribution. In this regard, the farm controller calculates the power reference signals for individual wind turbine controllers such that the sum of the power references tracks the power demanded by a system operator. Moreover, the reference signals are determined to reduce the load acting on wind turbines at low frequencies. Therefore, a trade-off is made for load and power control, which is formulated as an optimization problem. Afterwards, the optimization problem for the wind farm modeled as a bilinear control system is solved using an approximation method. The aim of this paper is to develop a controller for wind farms to optimize the load and power distribution. In this regard, the farm controller calculates the power reference signals for individual wind turbine controllers such that the sum of the power references tracks the power demanded by a system operator. Moreover, the reference signals are determined to reduce the load acting on wind turbines at low frequencies. Therefore, a trade-off is made for load and power control, which is formulated as an optimization problem. Afterwards, the optimization problem for the wind farm modeled as a bilinear control system is solved using an approximation method.

In this paper, a wind farm controller is developed that distributes power references among wind turbines while it reduces their structural loads. The proposed controller is based on a spatially discrete model of the farm, which delivers an approximation of wind speed in the vicinity of each wind turbine. The control algorithm determines the reference signals for each individual wind turbine controller in two scenarios based on low and high wind speed. In low wind speed, the reference signals for rotor speed are adjusted, taking the trade-off between power maximization and load minimization into account. In high wind speed, the power and pitch reference signals are determined while structural loads are minimized. To the best of authors’ knowledge, the proposed dynamical model is a suitable framework for control, since it provides a dynamic structure for behavior of the flow in wind farms. Moreover, the controller has been proven exceptionally useful in solving the problem of both power and load optimization on the basis of this model.

In this paper, an optimization approach has been developed for wind farms such that the reference signals for each wind turbine is produced and load reduction on each of them is taken into account. The proposed approach is based on a spatially discrete model of the farm which provides wake information and delivers an approximation of wind speed all over the farm. The farm controller can affect the generated wake of each wind turbine either by changing the pitch angle and power reference or rotor speed reference signals, and this is the motivation for the proposed optimization method for a farm. The problem of determining the reference signals for wind turbine controllers is considered separately for low and high wind speed regions. However, because of the complexity of the high wind speed scenario formulated with this approach, only the problem in low wind speed has been scrutinized. Accordingly, in low wind speed, the reference signals for rotor speed are adjusted, taking the trade-off between power maximization and load minimization into account.

The wind farm has an intrinsically distributed structure, where wind turbines are counted as subsystems of the distributed system. The coupling between the subsystems is the wind flow and the power reference set-points across the turbines, which are designed to provide the total wind farm power demand. Distributed controller design commences with formulating the problem, where a structured matrix approach has been put in practice. Afterwards, an H2 control design formulation is used to find the control signal set points for the wind farm to minimize structural loads on the turbine while providing the desired total wind farm power.

This work presents a control algorithm for wind farms that optimizes the power production of the farm and helps to increase the lifetime of wind turbines components. The control algorithm is a centralized approach, and it determines the power reference signals for individual wind turbines such that the structural loads of the wind turbines in low frequencies are reduced. The controller is relatively easy to implement on a wind farm, and in here the results of simulating the controller on a small wind farm is presented.

This paper describes the design procedure of an optimal wind farm controller. The controller optimizes the structural load and power production simultaneously, on the basis of an analytical wind farm model. The farm model delivers maps of wind, loads and energy in the wind farm. Moreover, the model computes the wind speed at the turbines, turbine bending moments and aerodynamic power and torque. The optimal control problem is formulated based on the model for two different wind directions.The controller determines the reference signals for each individual wind turbine controller in two scenarios based on low and high wind speed. In low wind speed, the reference signals for rotor speed are adjusted, taking the trade-off between power maximization and load minimization into account. In high wind speed, the power and pitch angle reference signals are determined while structural loads are minimized.

A wind deficit model for wind farms is developed in this work using finite volume method. The main question addressed here is to calculate approximately the wind speed in the vicinity of each wind turbine of a farm. The procedure followed is to solve the governing equations of flow for the whole wind farm. Spatial discretization is performed on a computational domain, using finite volume techniques. In ideal circumstances, wind speed will calculate on a fine mesh for the whole wind farm, where the effect of wind turbines is observed by means of their thrust coefficient. The end goal of this method is to assist the development of an analytical expression of an offshore wind farm wake. We focus in this work on wind deficit that can be used for better wind farm control strategies and load optimization.

To explore the awareness and attitudes of Iranian GPs in implementing evidence-based health promotion and disease prevention recommendations in primary care and to describe GPs' perceived barriers to implementing these recommendations.Our survey was carried out in 2007 in a random sample of GPs listed from an educational symposiums held for general practitioners. We developed and pre-tested a questionnaire, it was piloted with 417 GPs in general practitioners educational symposiums attending all parts of Iran. Those who did not respond received follow-up mailings and/or telephone calls. Our data management center started data entry and analyzing as soon as the questionnaires were sent back. Discrete variables were expressed as percentages and were compared with the chi-square test or Fisher's exact test as appropriate. Continuous variables were compared by means of the unpaired, two-sided t test. All statistical analyses were performed using SPSS software Version 13. Statistical significance was accepted for P0.05.Four hundred and seventeen GPs participated in the study. Although they believe they should advise preventive and health promotion activities, in practice, they are less likely to do so. The two most important barriers reported were weak patients' interest in screening tests no reimbursement for GPs. Associations between personal health behaviour and attitudes to health promotion or activities in prevention were found. GPs who exercised felt that they were more effective in helping patients to practice regular physical exercise than sedentary GPs. (P0.001).Significant gaps between GP's knowledge and practices persist in the use of evidence-based recommendations for health promotion and disease prevention in primary care.

Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.