Adequacy assessment of generating systems containing wind power considering wind speed correlation

A prototype of a wind power plant had been created using a ventilator as a generator spiner. This power plant utilizes wind speed as its propulsion. Electricity generated in the DC voltage form between 0 volts up to 7.46 volts. The MT3608 module is used to stabilize and raise the voltage installed in the input and output of the charging circuit. For instrument testing, the wind speed on 0 m/s up to 6 m/s interval used. Maximum output of this tool with a wind speed of 6 m/s is 7.46 volts.

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Multi-step real-time wind speed prediction based on convolution memory network

Wind Engineering ◽

10.1177/0309524x21998266 ◽

2021 ◽

pp. 0309524X2199826

Author(s):

Guowei Cai ◽

Yuqing Yang ◽

Chao Pan ◽

Dian Wang ◽

Fengjiao Yu ◽

...

Keyword(s):

Feature Extraction ◽

Wind Speed ◽

Real Time ◽

Wind Power ◽

Spatial Correlation ◽

Prediction Accuracy ◽

Spatial Information ◽

Spatial Association ◽

Spatial Feature ◽

Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

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Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

Energies ◽

10.3390/en14061587 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1587

Author(s):

Krzysztof Wrobel ◽

Krzysztof Tomczewski ◽

Artur Sliwinski ◽

Andrzej Tomczewski

Keyword(s):

Wind Speed ◽

Power Plant ◽

Wind Power ◽

Power Plants ◽

Control Method ◽

Wind Power Plant ◽

Switched Reluctance ◽

Wind Speeds ◽

Wind Speed Distribution ◽

And Control

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

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A Reliability Based Model for Wind Turbine Selection

International Journal of Renewable Energy Development ◽

10.14710/ijred.2.2.69-74 ◽

2013 ◽

Vol 2 (2) ◽

pp. 69-74 ◽

Cited By ~ 2

Author(s):

A.K. Rajeevan ◽

P.V. Shouri ◽

Usha Nair

Keyword(s):

Power Generation ◽

Wind Speed ◽

Wind Turbine ◽

Wind Power ◽

Point Of View ◽

Cube Root ◽

Mathematical Relationship ◽

Turbine Generator ◽

Wind Turbine Generator ◽

A Site

A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

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Airflow Characteristics According to the Change in the Height and Porous Rate of Building Roofs for Efficient Installation of Small Wind Power Generators

Sustainability ◽

10.3390/su13105688 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5688

Author(s):

Jangyoul You ◽

Kipyo You ◽

Minwoo Park ◽

Changhee Lee

Keyword(s):

Wind Speed ◽

Wind Power ◽

Turbulence Intensity ◽

Flow Characteristics ◽

High Porosity ◽

Power Generators ◽

Speed Reduction ◽

Reduction Effect ◽

Wind Power Generators ◽

The Impact

In this paper, the air flow characteristics and the impact of wind power generators were analyzed according to the porosity and height of the parapet installed in the rooftop layer. The wind speed at the top was decreasing as the parapet was installed. However, the wind speed reduction effect was decreasing as the porosity rate increased. In addition, the increase in porosity significantly reduced turbulence intensity and reduced it by up to 40% compared to no railing. In the case of parapets with sufficient porosity, the effect of reducing turbulence intensity was also increased as the height increased. Therefore, it was confirmed that sufficient parapet height and high porosity reduce the effect of reducing wind speed by parapets and significantly reducing the turbulence intensity, which can provide homogeneous wind speed during installation of wind power generators.

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Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽

10.3390/electronics10030261 ◽

2021 ◽

Vol 10 (3) ◽

pp. 261

Author(s):

Tianyang Liu ◽

Zunkai Huang ◽

Li Tian ◽

Yongxin Zhu ◽

Hui Wang ◽

...

Keyword(s):

Time Series ◽

Wind Speed ◽

Wind Turbine ◽

Wind Power ◽

Rapid Development ◽

Superior Performance ◽

Learning Platform ◽

Novel Approach ◽

Wind Power Forecast ◽

Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

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The Research of Wind Power Fluctuation in Different Time Intervals

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.953-954.458 ◽

2014 ◽

Vol 953-954 ◽

pp. 458-461

Author(s):

Yi Hui Zhang

Keyword(s):

Wind Speed ◽

Probability Distribution ◽

Wind Power ◽

Wind Farm ◽

Time Window ◽

Missing Information ◽

Power Fluctuation ◽

Time Intervals ◽

Trend Pattern ◽

Length Of Interval

Power from wind turbines is mainly related to the wind speed. Due to the influence of the uncertainty of the wind, intermittent and wind farm in units of the wake, wind power has fluctuations. Based on the field measurement, it is found that t location-scale distribution is suitable to identify the probability distribution of wind power variations. By analyzing the fluctuation of a single in different time intervals, we find that the distribution of wind power fluctuation possesses a certain trend pattern. With the length of the time window increasing, the fluctuations increase and some information has been missed. We define an index to calculate the quantity of missing information and can use that to evaluate whether a certain length of interval is acceptable.

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

Energies ◽

10.3390/en14144291 ◽

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.

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