scholarly journals A general method to estimate wind farm power using artificial neural networks

Wind Energy ◽  
2019 ◽  
Vol 22 (11) ◽  
pp. 1421-1432 ◽  
Author(s):  
Chi Yan ◽  
Yang Pan ◽  
Cristina L. Archer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Farm ◽  
Artificial Neural ◽  
General Method

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 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>

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.

Study of Wind Farm Power Output Predicting Model Based on Nonlinear Time Series

2014 ◽  
Vol 670-671 ◽  
pp. 1526-1529
Author(s):  
Yun Teng ◽  
Zhi Yao An ◽  
Xin Yu ◽  
Zhen Hao Wang ◽  
Yong Gang Zhang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Wind Power ◽  
Wind Farm ◽  
Great Influence ◽  
Engineering Application ◽  
Nonlinear Time Series ◽  
Predicting Model ◽  
Artificial Neural

To solve the problem of the variancy of the wind power when wind farm connect with the power grid, a wind power predicting model of wind farm based on double ANNs is proposed in the paper. Wind velocity and wind direction on wind farm are the key of wind power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure, are also great influence on it. The observed values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by the nonlinear time series ANNs model. The wind power predicting model consists of double artificial neural networks. The first is consisted of five artificial neural networks which is used to prediction the circumstance conditions time series, the second is employed to prediction the power of wind farm use predicting value of the five conditions. A series of simulation show that the results of the predicting model is acceptable in engineering application.

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