Browsing by Author "Ata, R"
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Item Assessment of optimum tip speed ratio in wind turbines using artificial neural networksYurdusev, MA; Ata, R; Çetin, NSWind turbine blade design depends on several factors, such as turbine profile used, blade number, power factor, and tip speed ratio. The key to designing a wind turbine is to assess the optimal tip speed ratio (TSR). This will directly affect the power generated and, in turn, the effectiveness of the investment made. TSR is suggested to be taken between 7 and 8 and in practice generally taken as 7 for a 3-blade network-connected wind turbine. However, the optimal TSR is dependent upon the profile type used and the blade number and could fall out of the boundaries suggested. Therefore, it has to be assessed accordingly. In this study, the optimal TSR and the power factor of a wind turbine are predicted using artificial neural networks (ANN) based on the parameters involved for NACA 4415 and LS-1 profile types with 3 and 4 blades. The ANN structure built is found to be more successful than the conventional approach in estimating the TSR and power factor. (c) 2005 Elsevier Ltd. All rights reserved.Item NEURAL PREDICTION OF POWER FACTOR IN WIND TURBINESAta, R; Cetin, NSThe power generated by wind turbines depends on several factors. One of them is the power factor also known as blade efficiency. In this study, the power factor is predicted using Artificial Neural Networks (ANN) and comparisons made with conventional model approach for the selected turbine profiles mostly used in practice. The study has shown that the prediction of power factors from seven input parameters by ANN yields better results than those of the conventional model.Item CONSTRUCTION AND ENERGY GENERATION OF 3 kW AUTONOMOUS WIND TURBINEAta, R; Çetin, NSThe autonomous wind turbine which has 3 kWh energy capacity has been installed Kirkagac MYO (Celal Bayar University) in Kirkagac. The established wind turbine is variable speed, three blades and its towers height is 15m. Electrical energy conversion is supplied via permanent-magnetic synchronous machine. The established system is operated as half of automatic via control component. C(p)-lambda curve has been drawn via required measurements obtained from turbine. At last, annual energy output values are calculated for wind turbine.Item An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbinesAta, R; Kocyigit, YThis paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method. (C) 2010 Elsevier Ltd. All rights reserved.Item AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINESAta, RThis paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the power factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the power factor was taken as output variable. After a successful learning and training process the proposed model produced reasonable mean errors. The results on a testing data indicate that the ANFIS model is found to be more successful than the ANN approach in estimating the power factor.Item Analysis of power losses in energy transmission systems including linear load by using artificial neural networksAta, R; Gunay, NThis paper presents the application of artificial neural networks (ANN) for analysis of power losses in an energy transmission network. Problem formulation and design of ANN for loss analysis is described. Due to the capability of parallel information processing of the ANN, the proposed method Is fast and vet accurate. Active and reactive powers of generators and loads, as well as the magnitudes of voltages at voltage-controlled buses are chosen as inputs to the ANN. System power losses are chosen as the outputs, Training data are obtained by load flow studies. Load flow studies for different system topologies are carried out and the results are compiled to form the training set. Numerical results are presented in the study to demonstrate the effectiveness of the proposed algorithm in terms of accuracy and speed.Item NEURAL PREDICTION OF WIND BLOWING DURATIONS BASED ON AVERAGE WIND SPEEDS FOR AKHISAR LOCATIONAta, RRenewable energy resources are widely preferred over conventional resources as they are environmentally favorable. Wind energy is one of the important renewable energy resources and has been widely developed recently. The energy produced from wind is dependent upon several factors. One of them is average wind speed and the other is wind blowing period. In this study, the wind blowing period is estimated based on annual average wind speed, Hellman coefficient and tower height using artificial neural networks (ANN). The results of ANN are compared with a conventional method in which Rayleigh distribution is employed.Item RETRACTED: Artificial neural networks applications in wind energy systems: a review (Retracted article. See vol. 84, pg. 173, 2018)Ata, RNeural networks approaches are becoming useful as an alternate way to classical methods. As a computation and learning paradigm, they are presented as a different modeling approach to solve complicated problems. They have been used to solve complicated practical problems in various areas, such as engineering, medicine, business, manufacturing, military etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, evaluation, classification, and control of complex systems. During the last three decades, artificial neural network have been extensively employed in numerous fields of science and technology. They are not programmed in the conventional procedure but they are trained using data exemplifying the behaviour of a system. This study presents various applications of neural networks used in wind energy systems. The applications of neural networks in wind energy systems could be grouped in three major categories: forecasting and prediction, prediction and control, identification and evaluation. The main purpose of this paper is to present an overview of the neural network applications in wind energy systems. Published literature presented in this study indicate the potential of ANN as a useful tool for wind energy systems. Author strongly believes that this survey will be very much useful to the researchers, scientific engineers working in this area to find out the relevant references and current state of the field. (C) 2015 Elsevier Ltd. All rights reserved.