Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

Sensitivity of Array-Like and Optimized Wind Farm Output to Key Factors and Choice of Wake Models

2013 ◽  
Author(s):  
Weiyang Tong ◽  
Souma Chowdhury ◽  
Ali Mehmani ◽  
Jie Zhang ◽  
Achille Messac
Keyword(s):  
Power Generation ◽  
Sensitivity Analysis ◽  
Wind Speed ◽  
Power Output ◽  
Wind Farm ◽  
Generation Model ◽  
Optimization Strategy ◽  
Key Factors ◽  
Wind Farm Design ◽  
Farm Design

The creation of wakes, with unique turbulence characteristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.

Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Weiyang Tong ◽  
Souma Chowdhury ◽  
Ali Mehmani ◽  
Achille Messac ◽  
Jie Zhang
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Farm ◽  
Input Parameter ◽  
Wind Farms ◽  
Key Factors ◽  
Sensitivity Indices ◽  
Wind Farm Design ◽  
The Impact ◽  
Farm Design

In conventional wind farm design and optimization, analytical wake models are generally used to estimate the wake-induced power losses. Different wake models often yield significantly dissimilar estimates of wake velocity deficit and wake width. In this context, the wake behavior, as well as the subsequent wind farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these key factors, particularly under the application of different wake models, is paramount to reliable quantification of wind farm power generation. Such an understanding is however not readily evident in the current state of the art in wind farm design. To fill this important gap, this paper develops a comprehensive sensitivity analysis (SA) of wind farm performance with respect to the key natural and design factors. Specifically, the sensitivities of the estimated wind farm power generation and maximum farm output potential are investigated with respect to the following key factors: (i) incoming wind speed, (ii) ambient turbulence, (iii) land area per MW installed, (iv) land aspect ratio, and (v) nameplate capacity. The extended Fourier amplitude sensitivity test (e-FAST), which helpfully provides a measure of both first-order and total-order sensitivity indices, is used for this purpose. The impact of using four different analytical wake models (i.e., Jensen, Frandsen, Larsen, and Ishihara models) on the wind farm SA is also explored. By applying this new SA framework, it was observed that, when the incoming wind speed is below the turbine rated speed, the impact of incoming wind speed on the wind farm power generation is dominant, irrespective of the choice of wake models. Interestingly, for array-like wind farms, the relative importance of each input parameter was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have marginal impact on the sensitivity indices.

scholarly journals Method for Determining the Maximum Allowable Capacity of Wind Farm Based on Box Set Robust Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lihui Guo ◽  
Hao Bai
Keyword(s):  
Wind Speed ◽  
Robust Optimization ◽  
Wind Power ◽  
Optimization Model ◽  
Wind Farm ◽  
Optimization Theory ◽  
Wind Farms ◽  
Bilevel Optimization ◽  
Grid Connection ◽  
Node Voltage

With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm.

The Application of Wind Speed Numerical Simulation in Wind Power Generation

2013 ◽  
Vol 380-384 ◽  
pp. 3370-3373 ◽  
Author(s):  
Li Yang Liu ◽  
Jun Ji Wu ◽  
Shao Liang Meng
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Power Grid ◽  
Wind Farms ◽  
Stable Operation ◽  
Wind Gust ◽  
Gradual Change ◽  
Wake Effect

With the massive development and application of wind energy, wind power is having an increasing proportion in power grid. The changes of the wind speed in a wind farm will lead to fluctuations in the power output which would affect the stable operation of the power grid. Therefore the research of the characteristics of wind speed has become a hot topic in the field of wind energy. In the paper, the wind speed at the wind farm was simulated in a combination of wind speeds by which wind speed was decomposed of four components including basic wind, gust wind, stochastic wind and gradient wind which denote the regularity, the mutability, the gradual change and the randomness of a natural wind respectively. The model is able to reflect the characteristics of a real wind, easy for engineering simulation and can also estimate the wind energy of a wind farm through the wind speed and wake effect model. This paper has directive significance in the estimation of wind resource and the layout of wind turbines in wind farms.

scholarly journals Optimal Irregular Wind Farm Design for Continuous Placement of Wind Turbines with a Two-Dimensional Jensen-Gaussian Wake Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2660 ◽  
Author(s):  
Longyan Wang ◽  
Yunkai Zhou ◽  
Jian Xu
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Power Losses ◽  
Wake Effect ◽  
Coordinate Method ◽  
Wind Farm Design ◽  
Wake Model ◽  
Farm Design

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

2012 ◽  
Vol 224 ◽  
pp. 401-405
Author(s):  
Xi Yun Yang ◽  
Peng Wei ◽  
Huan Liu ◽  
Bao Jun Sun
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Wind Farm ◽  
Prediction Method ◽  
Power Curve ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Prediction

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

Reliability Assessment of Wind Power System Considering Multi-Objective Models

2012 ◽  
Vol 608-609 ◽  
pp. 742-747
Author(s):  
Chun Hong Zhao ◽  
Lian Guang Liu ◽  
Zi Fa Liu ◽  
Ying Chen
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
System Reliability ◽  
Wind Farm ◽  
Wind Farms ◽  
Test System ◽  
Power System Reliability ◽  
Multi Objective ◽  
Wind Power System

The integration of wind farms has a significant impact on the power system reliability. An appropriate model used to assess wind power system reliability is needed. Establishing multi-objective models (wind speed model, wind turbine generator output model and wind farm equivalent model) and based on the non-sequential Monte Carlo simulation method to calculate risk indicators is a viable method for quantitatively assessing the reliability of power system including wind farms. The IEEE-RTS 79 test system and a 300MW wind farm are taken as example.The calculation resluts show that using the multi-objective models can improve accuracy and reduce error; the higher average wind speed obtains the better system reliabitity accordingly.

Study of Combined Forecast Model of Wind Power Output Based on GM-Weibull Wind Speed Distribution

2014 ◽  
Vol 670-671 ◽  
pp. 1566-1569
Author(s):  
Yun Teng ◽  
Qian Hui ◽  
Xin Yu ◽  
Zheng Liu ◽  
Yong Gang Zhang
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Wind Power ◽  
Power Output ◽  
Wind Farm ◽  
Distribution Model ◽  
Grey Theory ◽  
Grey Prediction ◽  
Speed Distribution ◽  
Wind Speed Distribution

The grey theory is employed to establish the grey prediction-wind speed Weibull distribution model and calculate the Weibull distribution parameters according to the randomness and intermittence of the wind power output. The wind speed distribution of the wind farm and the effective wind power density are predicted accurately, the wind power and the electric fan efforts in generating capacity and other important data can be obtained according to the actual terrain wind farm wind speed data.

Sign in / Sign up

Export Citation Format

Share Document