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 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.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

scholarly journals Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 749 ◽  
Author(s):  
Jorge E. Jiménez-Hornero ◽  
Inés María Santos-Dueñas ◽  
Isidoro García-García
Keyword(s):  
Neural Networks ◽  
Acetic Acid ◽  
Artificial Neural Networks ◽  
First Principles ◽  
Goodness Of Fit ◽  
Black Box ◽  
Operating Conditions ◽  
Box Models ◽  
Artificial Neural ◽  
Black Box Models

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

An Estimation of 3-Way Catalyst Performance Using Artificial Neural Networks During Idle Speed

2004 ◽  
Author(s):  
P. N. Botsaris ◽  
D. Bechrakis ◽  
P. D. Sparis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Expert Knowledge ◽  
Operating Conditions ◽  
Experimental Results ◽  
Outlet Temperature ◽  
Practical Applications ◽  
Idle Speed ◽  
Artificial Neural

The intelligent control as fuzzy or artificial is based on either expert knowledge or experimental data and therefore it possesses intrinsic qualities like robustness and ease implementation. Lately, many researchers present studies aim to show that this kind of control can be used in practical applications such as the idle speed control problem in automotive industry. In this study, an estimation of an automobile three-way catalyst performance with artificial neural networks is presented. It may be an alternative approach for an on board diagnostic system (OBD) to predict the catalyst performance. This method was tested using data sets from two kind of catalysts, a brand new and an old one on a laboratory bench at idle speed. The catalyst operation during the “steady state” phase (the phase that the catalyst has reached its operating conditions and works normally) is examined. Further experiments are needed for different catalyst typed before the methods is proposed generally. It consists of 855 elements of catalyst inlet-outlet temperature difference (DT), hydrocarbons (HC), and carbon monoxide (CO) and carbon dioxide (CO2) emissions. The simulation: detects the values of HC, CO, CO2 using the DT as an input to our network forms a neural network. Results showed serious indications that artificial neural networks (or fuzzy logic control laws) could estimate the catalyst performance adequately depending their training process, if certain information about the catalyst system and the inputs and output of such system are known. In this study the “steady state” period experimental results are presented. In this paper the “steady state” period experimental results are presented.

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

scholarly journals Process Configuration Studies of Methanol Production via Carbon Dioxide Hydrogenation: Process Simulation-Based Optimization Using Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6608
Author(s):  
Prapatsorn Borisut ◽  
Aroonsri Nuchitprasittichai
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Production Cost ◽  
Operating Conditions ◽  
Price Sensitivity ◽  
Methanol Production ◽  
Artificial Neural

Methanol production via carbon dioxide (CO2) hydrogenation is a green chemical process, which can reduce CO2 emission. The operating conditions for minimum methanol production cost of three configurations were investigated in this work. An artificial neural network with Latin hypercube sampling technique was applied to construct model-represented methanol production. Price sensitivity was performed to study the impacts of the raw materials price on methanol production cost. Price sensitivity results showed that the hydrogen price has a large impact on the methanol production cost. In mathematical modeling using feedforward artificial neural networks, four different numbers of nodes were used to train artificial neural networks. The artificial neural network with eight numbers of nodes showed the most suitable configuration, which yielded the lowest percent error between the actual and predicted methanol production cost. The optimization results showed that the recommended process design among the three studied configurations was the process of methanol production with two reactors in series. The minimum methanol production cost obtained from this configuration was $888.85 per ton produced methanol, which was the lowest methanol production cost among all configurations.

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