This paper proposes a modeling method for wind farms from the angle of the predictability of the stochastic wind velocity and direction,and then establishes the wind farm stochastic model.The operation characteristics of the wind farm is simulated to serve the goal of predictability and controllability of large-scaled wind power.Additionally,the rationality of this modeling method is validated via the comparison between the equivalent model and the detailed model of the wind farm.Finally the paper concludes that the influence of the stochastic volatility of wind velocity and direction should be taken into consideration when the model is established to study the dynamic behavior of wind power and its influence on power system.

This paper focuses on the identification of equivalent model parameters of the wind farm as the identification is significantly meaningful both in theory and of great practical values.The paper concludes that analyzing the static condition and dynamic procedures before and after a disturbance occurring to the farm proves that only the equivalent model parameters can be identified.

The national standard GB/T 19963-2011 Technical Rules for Connecting Wind Farm to Power System was released in December 2011 and came into force from June 1,2012.This paper focuses on interpreting the necessity of standard formulation and the detailed standard requirements,and conducting a detailed comparison study on the grid codes for connecting wind power to power system in other countries with many wind power installed capacities based on analysing the status of wind power development and the technical problems encountered.The formulation and implementation of the national wind power integration code will contribute a lot to upgrading the wind turbine technology and ensuring the secure and stable operation of the power system incorporating large-scale wind power.

The short term wind speed prediction, a prediction approach was presented. This approach employs the wind speed from the numerical weather prediction-NWP as an input data, a roughness change model and orographical change model are used to model the local effects of a wind farm. The predicted wind speed was compared with the measured wind speed, under the typical wind conditions. The result showed that the prediction value can basically meet the requirements of prediction precision; the accuracy could change depending on how drastically the wind speed changes. According to the error analysis, the accuracy of the NWP wind speed is the most important fact, and which affects the prediction result.

The stochastic properties of wind speed and direction affect simultaneously the output power and dynamic characteristic of wind power generation system. The description of stochastic properties of wind speed and direction is essential to study the control, output power and dynamic characteristic of wind power generation system. So the stochastic model of wind speed time series affected by wind direction was set up based on sample analysis of correlation and Box-Jenkins modeling. The stochastic model of wind speed affected by wind direction were presented in Matlab/Simulink and intergrated to DIgSILENT/PowerFactory. The rationality of model was verified by simulation.

Wind power prediction is a very effective way to increase the wind power penetration and improve the security and economy of the power system.Wind power prediction based on physical principle is independent on the measured data,which is very suitable to the newly-built wind farms.This paper studied on the short term wind power prediction based on physical principle,a prediction approach for engineering application was presented.An analytic principle Was employed to analyze the local effect of wind farm and wake effect of wind turbines.The predicted wind power was compared with the measured power output of a study wind farm,under the typical wind power output.The result shows that the prediction approach can predict the typical power output very well,and the precision of global prediction and point-by-point prediction can meet the requirement of engineering application.Because of the limitation of numerical weather prediction(NWP)model and grid resolution,the prediction result is not very good when the wind changes drastically;it could improve the prediction result when a more accurate NWP data is employed.

WAsP is not able to simulate the flow of wind field in the complex terrain, because of the model limitation, which is one of the main error source of the complex terrain wind resource assessment. This paper studies the error of calculated energy production by WAsP when it is applied to the complex terrain. An error evaluation method is presented and verified by the actual production data from an existing wind farm. The result shows that the presented error evaluation method is very effective and has a good practical value.

The wind speed model considering the wake effect and wind farm model based on the wind speed were established in order to analyze the voltage stability under wind fluctuation. The wind farm dynamic characteristic was simulated using different wind farm model separately. The results show that the dynamic character of wind farm is illustrated well using the wind farm model considering the wake effect and turbine layout comparing with the aggregated single turbine model. The voltage fluctuations of wind farm is studied using different wind turbines (fixed speed wind turbine and doubly fed wind turbine) in farm to verify the effect on power system voltage.

The installed capacity of a large scale wind power plant will be up to a number of hundreds MW, and the wind power is transmitted to load centers through long distance transmission lines with 220 kV, 500 kV, or 750 kV. Therefore, it is necessary not only considering the power transmission line between a wind power plant and the first connection node of the power network, but also the power network among the group of those wind power plants in a wind power base, the integration network from the base to the existed grids, as well as the distribution and consumption of the wind power generation by loads. Meanwhile, the impact of wind power stochastic fluctuation on power systems must be studied. In recent years, wind power prediction technology has been studied by the utilities and wind power plants. As a matter of fact, some European countries have used this prediction technology as a tool in national power dispatch centers and wind power companies.

It is of significance to forecast output power of wind farm for the operation of power grid to which large amount of wind power is connected.By use of BP neural network, radial basis function neural network and support vector machine, a combination forecasting model for output power of wind farm is built.The weights are calculated by three methods, i.e., equal weight average method, covariance optimization combination forecast and time-varying weight combination forecast.Research results show that the forecast accuracy from different methods is diverse one another;even though a method can offer high forecast accuracy in total, at individual point the forecast error of this method may be larger, however combination forecasting model can avoid larger forecast error in each point, so it is favorable to improve forecast accuracy.

Measure-Correlate-Predict (MCP) is not only a primary technical method to evaluate the development level of wind resource of a wind farm in plan, but it can also determine the annual energy production. The result of MCP can be influenced by the long term wind reference data, which means that if the long term wind reference data is low in credibility, the result of MCP is also unreliable; and then the wind resource assessment of the wind farm in plan can be false. However, in engineering practice, the long term wind reference data from meteorological stations can be affected by some objective factors, such as the new buildings around the wind masts; small trees around the wind masts can grow up to 20m or even higher, which would also affect the wind data. The NCEP/NCAR reanalysis dataset, which are produced by National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR), is widely used as an alternative long term wind reference data. The dataset is available from January 1 in 1948 to the present and data samples are recorded every 6 hours. Surface wind data and 10 m wind data are available and are saved on a grid at 2.5 degree resolution at different air pressure levels ranging from 10 hPa to 1000 hPa. This paper studies the application of the NCEP/NCAR reanalysis dataset in the wind resource assessment, because of the low resolution of the NCEP/ NCAR reanalysis dataset and the general MCP methods, such as the regression method, the WeibuU scale method and the matrix method, are not sufficient any more; therefore, a new MCP method, named as wind index, is presented. For evaluate the feasibility of the dataset and the new MCP method, a ease study is carried out. The calculated energy production of the studied wind farm, which is based on the long term wind reference data from meteorological stations and NCEP/ NCAR reanalysis dataset are compared. The result shows that the calculated energy production is very close to real energy production, which means that different long term reference wind data and different MCP methods show almost same development level of wind resource. Therefore, it can be concluded that the combination of the NCEP/NCAR reanalysis dataset with wind index MCP method is a very reliable MCP calculation principle and can have a good practical value.

WAsP is not able to simulate the flow of wind field in the complex terrain,because of the model limitation,which induces some errors when applies it to the wind resource assessment for complex terrain.At present,the RIX method is the most popular method for error evaluation of wind speed,but how to evaluate the error of energy production,there are not any effective methods yet.This paper studies the error of calculated energy production by WAsP when it was applied to the complex terrain.An error evaluation method is presented and verified by the actual production data from an existing wind farm.The result shows that the presented error evaluation method is very effective and has a good practical value.

The stability of wind farms is the primal problem in the analysis of wind farm integration with the increment of wind farm penetration. The transient stability of wind farms is denoted by the critical clean time(CCT) of an equivalent wind turbine on the grid fault. The CCT of the wind farm is influenced by the mechanical and electrical parameters of wind turbines, the integration of wind farms to the power system,the running of wind farms, and so on. First, the CCT of a fix-speed wind turbine and the influence on the CCT by the relaxation of turbine shaft were determined. Second, many factors influencing the CCT of the wind farm consisted by fix-speed wind turbines were studied by simulation in this paper. Finally some useful conclusions were educed.

The models of conventional wind turbines with an asynchronous generator and power system are developed in DIgSILENT/Power Factory. Based on the model, the impacts of wind turbines with an asynchronous generator on the small-signal stability and damping characteristics of power system are analysed. An improved pitch control scheme is presented by introducing a frequency deviation signal into the control system. When low frequency oscillation happens, the wind turbine output is regulated to correlate with system oscillation frequencies. Both frequency domain analysis and time domain simulation results demonstrate that the improved pitch control methods significantly improve the damping characteristics of power system. Therefore the power oscillations are mitigated and the system dynamic stability is enhanced effectively in the system.

Wind power prediction is important to the operation of power system with comparatively large mount of wind power. The wind power prediction methods were classified into several kinds. An artificial neural network (ANN) model for wind power prediction was constructed according to the wind power influence factors. Then the impacts of real time measured power and the atmospheric data at different heights on prediction results were analyzed. Besides, another ANN model for error band prediction was also built. The results indicate that the ANN structure and the training sample have some impact on the prediction precision. The real time measured power as input will improve the precision of 30 min ahead prediction, however will decrease the precision of 1h ahead prediction. The results which using the atmospheric data at all different heights as input have a higher accuracy when compared with the results using hub height data only. The designed ANN can forecast the error band.

A summarization of the impact of large scale wind power on power systems. The characteristic of wind power development is concluded, The impact includes reactive power and voltage problem, transient stability problem, frequency stability problem, power quality problems. Some solution and research direction is presented for different questions. Two important research areas are discussed, one is wind farm integration control technology, the other one is wind power prediction technology.

The transient stability of wind turbine during power fault and effect on power network are studied.Based on the equal area rule,the CCT(Critical Clearing Time) of induction generator is determined with its torque -slip curve and mechanical torque curve.Since the released energy during the relaxation process of wind turbine shaft(two -mass model) increases its speed,making its acceleration area increased and bigger than its deceleration area,the induction generator CCT of constant speed wind turbine is shorter than that of normal one,by which the CCT of wind turbine is determined.Simulative analysis points out that,the bigger the wind turbine shaft stiffness is,the larger the CCT of constant speed wind turbine is.When the shaft stiffness of wind turbine is big enough,the CCT of constant speed wind turbine will not increase along with the stiffness increase,it means that,the wind turbine shaft is of lump-mass model.

The models of the wind turbine generator and power system are established with power system analysis software DIgSILENT / Power Factory,including fixed speed-wind turbine generator and variable-speed wind turbine doubly-fed induction generator(VSWT-DFIG).The 3-phase symmetrical short circuit fault near wind farm is set for theriotic and simulative study.The short circuit waveforms are compared between wind turbine generators and synchronous generator with same capacity.The short-circuit busbar currents near wind farm are calculated before and after its connection to grid.Its incremental percents is also calculated to evaluate the contribution of wind farm to system short curcuit current.The results demonstrate that the influence of wind farm on the short-circuit current of nearby node must be considered in selecting the electrical devices near wind farm and calculating their thermal stabilities.

A power system stabilizing technology is introduced into the wind turbine with a doubly fed induction generator(DFIG),by use of the slip signal as feedbacks into the rotor side converter control model.During the power oscillation of a power system,a damping active power is injected into the network from the DFIG machine via regulating the rotor excitation voltage angle.This will improve the overall system damping characteristic.A testing system including a wind farm is established in DIgSILENT/Power Factory.The eigenvalue analysis and time response simulation studies are conducted with and without the proposed control model.The results illustrate those wind turbines using the proposed controller can improve the system damping.The power oscillations are restrained and the dynamic stability enhanced.

The paper presents a method to enhance the transient voltage stability of fixed speed wind turbine and variable rotor resistance generator based wind farm using static synchronous compensator (STATCOM). The voltage stability characteristic of induction generator is analyzed. The induction generators absorb lots of reactive power during system fault, so the voltage decreases. The STATCOM can provide reactive power support and avoid tripping of the wind farm. The induction generator based wind turbine models, variable rotor resistance control model and STATCOM model were implemented in the power system simulation program DIgSILENT/PowerFactory. The contribution to the transient voltage stability was verified by power system simulation containing a large wind farm. In the simulation two generator types are considered, one is cage rotor induction generator and the other is wound rotor induction generator with variable rotor resistance. According to the simulation results, it can be concluded that STATCOM provides reactive power support to the voltage recovery of generators, the voltage restore rapidly after the faults cleared, so the low voltage ride through (LVRT) capability is improved, ensures the continuous operation of wind turbines and the security and stability of the power network.