scholarly journals Downscaling and Verification of Maximum Wind Speeds by Using Artificial Neural Networks for New European Wind Atlas and Wind Farm Data 

2021 ◽  
Author(s):  
Melek Akın ◽  
Ahmet Öztopal ◽  
Ahmet Duran Şahin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Wind Farm ◽  
Wind Farms ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Artificial Neural

<p>As is known, wind is a renewable and non-polluting energy resource. In addition, there is no transportation problem in wind energy and it does not require very high technology for electricity generation. Wind turbines are used for electricity generation from kinetic energy of wind. In the point of power curves of these turbines, wind speed must be a certain band. Generally, they do not generate electricity cut-in wind speed that is between 0 and 4 m/s and cut out wind speed that is over 20-25 m/s. Over cut-out values cause breaking down of wind turbines, because high wind speeds create extra mechanical loads on them. Therefore, maximum/extreme winds and their estimation and prediction carry weight in terms of energy generation.</p><p>New European Wind Atlas (NEWA) is the project, within the scope of ERANET+ Program, and the attendants are Belgium, Denmark, Germany, Latvia, Portugal, Spain, Sweden, and Turkey. The aim of NEWA is to present a new wind atlas to the wind industry. In this project, the physical model used for obtaining wind speeds is a numerical weather prediction model named Weather Research and Forecasting (WRF).</p><p>One of the methods, which are developed by imitating of biological properties of living forms in a virtual environment, is Artificial Neural Networks (ANNs). Stimulations taken from the environment by using sense organs are transmitted to brain whereby neurons in a body and brain makes a decision towards these stimulations. That is the working form of ANNs. Moreover, ANNs can be thought as a black box, which processes given data and produces outputs against inputs. Furthermore, they are a method of Artificial Intelligence.</p><p>In this study, maximum wind speeds of 4 different wind farms in Turkey were estimated by using a downscaling method based on ANNs and wind data which were produced in grid points of NEWA Project. Besides that, 8 different levels (10, 50, 75, 100, 150, 200, 250, and 500 m) for each wind farm were considered. As a result of determining the best ANN architectures with sensitivity analysis, it was seen that Levenberg-Marquardt Backpropagation (trainlm) approach as a training algorithm and 9 neurons in each layer are common traits of best ANN architectures. In addition, 50 m for 2 wind farms and 10 m with 75 m for others were determined as an optimum downscaling levels. Moreover, according to downscaling results, correlation values were calculated around 0.80.</p><p><strong>Key Words: </strong>ANN, Downscaling, Maximum wind, NEWA, Turkey, Wind farm.</p>

Case study: Automated recognition of wind farm sound using artificial neural networks

2020 ◽  
Vol 68 (2) ◽  
pp. 157-167
Author(s):  
Gino Iannace ◽  
Amelia Trematerra ◽  
Giuseppe Ciaburro
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Farm ◽  
Power Level ◽  
Electrical Power ◽  
Wind Farms ◽  
Noise Pollution ◽  
Clean Energy ◽  
Operating Conditions ◽  
Artificial Neural

Wind energy has been one of the most widely used forms of energy since ancient times, with it being a widespread type of clean energy, which is available in mechanical form and can be efficiently transformed into electricity. However, wind turbines can be associated with concerns around noise pollution and visual impact. Modern turbines can generate more electrical power than older turbines even if they produce a comparable sound power level. Despite this, protests from citizens living in the vicinity of wind farms continue to be a problem for those institutions which issue permits. In this article, acoustic measurements carried out inside a house were used to create a model based on artificial neural networks for the automatic recognition of the noise emitted by the operating conditions of a wind farm. The high accuracy of the models obtained suggests the adoption of this tool for several applications. Some critical issues identified in a measurement session suggest the use of additional acoustic descriptors as well as specific control conditions.

scholarly journals A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1845
Author(s):  
Annalisa Santolamazza ◽  
Daniele Dadi ◽  
Vito Introna
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Machine Learning Techniques ◽  
Real Time Control ◽  
Time Control ◽  
Artificial Neural ◽  
Using Data

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.

scholarly journals Using artificial neural networks to determine the location of wind farms. Miedzna district case study

2016 ◽  
Vol 30 (1) ◽  
pp. 101-111 ◽  
Author(s):  
Krzysztof Pokonieczny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Farm ◽  
Digital Terrain Model ◽  
Wind Farms ◽  
Shuttle Radar Topography Mission ◽  
Point Of View ◽  
Artificial Neural ◽  
Trained Neural Network ◽  
Area Classification

Abstract The article concerns issues pertaining to of selecting suitable areas for wind farms. The basic assumption of the study was to take into account both criteria related to the profitability of this type of power plant, as well as public interest, which means the harmonious and not burdensome functioning of these installations for local communities. The problem of wind farm localization may be solved by the application of artificial neural networks (ANN), which are a computational intelligence element. In the conducted analysis, the possibility of wind farm localization was considered for the primary grid field with dimensions of 100 by 100 m. To prepare the training set, topographic vector data from the VMap L2 and SRTM (Shuttle Radar Topography Mission) digital terrain model were used. For each 100-meter × 100-meter grid, the input data was prepared, consisting of the factors which are important from the point of view of wind farm localization (forests, rivers, built-up areas etc.). Studies show that a properly trained neural network (using a representative number of samples and for the appropriate architecture), allows to process automation area classification in terms of placement on the wind turbines.

About the Extension of Wind Turbine Power Curve in the High Wind Region

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Test Case ◽  
Power Curve ◽  
High Wind ◽  
High Wind Speed ◽  
Wind Speeds

The financial sustainability and the profitability of wind farms strongly depend on the efficiency of the conversion of wind kinetic energy. This motivates further research about the improvement of wind turbine power curve. If the site is characterized by a considerable occurrence of very high wind speeds, it can become particularly profitable to update the power curve management. This is commonly done by raising the cut-out velocity and the high wind speed cut-in regulating the hysteresis logic. Doing this, on one side, the wind turbine possibly undergoes strong vibration and loads. On the other side, the energy improvement is almost certain and the point is quantifying precisely its magnitude. In this work, the test case of an onshore wind farm in Italy is studied, featuring 17 2.3 MW wind turbines. Through the analysis of supervisory control and data acquisition (SCADA) data, the energy improvement from the extension of the power curve in the high wind speed region is simulated and measured. This could be useful for wind farm owners evaluating the realistic profitability of the installation of the power curve upgrade on their wind turbines. Furthermore, the present work is useful for the analysis of wind turbine behavior under extremely stressing load conditions.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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.

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