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Since the established wind farms (WFs) have a strong demand for reducing maintenance costs, the fatigue load suppression of wind turbines (WTs) has become more important in WFs. To suppress the fatigue load during operation, the traditional schemes are tried by allocating the optimized power setting value to each WT, which does not directly point to the key operating parameters that affect fatigue. In response to this, an advanced WT control model with two-degree-of-freedom (2Dof) is applied in this paper to ensure that the generator torque and pitch angle (two key parameters affecting fatigue load) of each WT are directly controlled. In order to achieve effective control of the above two parameters in multiple WTs, a hierarchical distributed control architecture with dynamic-local-reference is proposed to jointly optimize the above two configurable parameters at both the WT and WF levels. Several cases are compared to verify the proposed method, and simulation results show that the proposed 2Dof control model for WT and distributed control framework for WF can significantly reduce the fatigue load of each WT while completing the active power command of the whole WF. • Two-degree-of-freedom wind turbine control model is applied in wind farms. • Distributed model predictive control (DMPC) framework is adopted. • Dynamic WT reference power algorithm is proposed based on DMPC. • Fatigue load of all wind turbines in the wind farm are significantly reduced. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a system of supervision and operation of a new structure wherein a large wind farm is connected to an electrical grid. The farm is managed in such a manner that it can produce the power needed by the grid system. The supervision algorithm is used to distribute the active and reactive power references to the wind turbines proportionally. Based on the aerodynamic power and wind speed of each turbine, the active and reactive power references are produced individually. By using the vector field oriented control, each doubly fed induction generator is controlled through the rotor, which is connected to the two-level pulse width modulation converter. The close loop control is used to provide a constant DC voltage using a five-level neutral point clamped converter. The five-level neutral point clamped converter allows also the adaptation of the voltage level to the electrical grid with better resolution waveform. The analysis of the simulation results shows the effectiveness of the proposed system. [Copyright &y& Elsevier]

Abstract: In this paper, a hybrid forecasting approach, which combines the Ensemble Empirical Mode Decomposition (EEMD) and the Support Vector Machine (SVM), is proposed to improve the quality of wind speed forecasting. The essence of the methodology incorporates three phases. First, the original data of wind speed are decomposed into a number of independent Intrinsic Mode Functions (IMFs) and one residual series by EEMD using the principle of decomposition. In order to forecast these IMFs, excepting the highest frequency acquired by EEMD, the respective estimates are yielded using the SVM algorithm. Finally, these respective estimates are combined into the final wind speed forecasts using the principle of ensemble. The proposed hybrid method is examined by forecasting the mean monthly wind speed of three wind farms located in northwest China. The obtained results confirm an observable improvement for the forecasting validity of the proposed hybrid approach. This tool shows great promise for the forecasting of intricate time series which are intrinsically highly volatile and irregular. [Copyright &y& Elsevier]

Abstract: Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The model considers wake loss, which can be calculated based on wind turbine locations, and wind direction. Since the turbine layout design is a constrained optimization problem, for ease of solving it, the constraints are transformed into a second objective function. Then a multi-objective evolutionary strategy algorithm is developed to solve the transformed bi-criteria optimization problem, which maximizes the expected energy output, as well as minimizes the constraint violations. The presented model is illustrated with examples as well as an industrial application. [Copyright &y& Elsevier]