Intelligent optimization of active power output of wind farm during fault recovery with wake effect

2018 ◽  
Vol 22 (S2) ◽  
pp. 4085-4090
Author(s):  
Jingyu Li ◽  
Ming Zhou ◽  
Gengyin Li
2013 ◽  
Vol 291-294 ◽  
pp. 461-466
Author(s):  
Guo Bing Qiu ◽  
Wen Xia Liu ◽  
Jian Hua Zhang

Considering the randomness of wind speed and wind direction, the partial wake effect between wind turbines (WTs) in complex terrain was analyzed and a multiple wake model in complex terrain was established. Taking the power output characteristic of WT into consideration, a wind farm reliability model which considered the outages of connection cables was presented. The model is implemented in MATLAB using sequential Monte Carlo simulation and the results show that this model corrects the power output of wind farm, while improving the accuracy of wind farm reliability model.


2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.


Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan

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.


2018 ◽  
Vol 8 (12) ◽  
pp. 2660 ◽  
Author(s):  
Longyan Wang ◽  
Yunkai Zhou ◽  
Jian Xu

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.


Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4848
Author(s):  
Shijia Zhou ◽  
Fei Rong ◽  
Xiaojie Ning

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.


2021 ◽  
Vol 11 (1) ◽  
pp. 35-48
Author(s):  
Mohammed Amine Hassoine ◽  
Fouad Lahlou ◽  
Adnane Addaim ◽  
Abdessalam Ait Madi

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have  been evaluated and compared to observational data, and also  with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of  10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.


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