Fatih Üneş

Estimating the energy production of the wind turbine using artificial neural network

Abstract Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (Pmin) and maximum (Pmax) power generation values as well as the total power generation (Psum) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, Pmin, Pmax, and Psum, were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models. Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phase of study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.

Analysis of plunging phenomenon in dam reservoirs using three-dimensional density flow simulations

Density flow is investigated in a three-dimensional model through a dam reservoir with diverging and sloping bottom channels. When an inflow of higher density enters ambient dam reservoir water, it plunges below the ambient water and becomes density underflow. In the present model, nonlinear and unsteady continuity, momentum, energy, and turbulence model equations are formulated in the Cartesian coordinates. The k–e turbulence model is used with an extension to include production or destruction of turbulent kinetic energy. To investigate the Coriolis force effect on the density flow in a dam reservoir, a Coriolis force parameter is included in the governing equations. The equations of the model are solved based on the initial and boundary conditions of the dam reservoir flow for a range of bottom slopes and divergence angles. In this paper, variation in density flow parameters, such as velocity, temperature, and turbulence viscosity through the dam reservoir, is investigated. Moreover, mixing rate, plungi...

Investigation of seasonal thermal flow in a real dam reservoir using 3-D numerical modeling

Abstract Investigations indicate that correct estimation of seasonal thermal stratification in a dam reservoir is very important for the dam reservoir water quality modeling and water management problems. The main aim of this study is to develop a hydrodynamics model of an actual dam reservoir in three dimensions for simulating a real dam reservoir flows for different seasons. The model is developed using nonlinear and unsteady continuity, momentum, energy and k-ε turbulence model equations. In order to include the Coriolis force effect on the flow in a dam reservoir, Coriolis force parameter is also added the model equations. Those equations are constructed using actual dimensions, shape, boundary and initial conditions of the dam and reservoir. Temperature profiles and flow visualizations are used to evaluate flow conditions in the reservoir. Reservoir flow’s process and parameters are determined all over the reservoir. The mathematical model developed is capable of simulating the flow and thermal characteristics of the reservoir system for seasonal heat exchanges. Model simulations results obtained are compared with field measurements obtained from gauging stations for flows in different seasons. The results show a good agreement with the field measurements.

3-D Numerical Simulation of a Real Dam Reservoir: Thermal Stratified Flow

Investigations indicate freshwater sources are dwindling day-to-day and becoming contaminated throughout the world due to environmental problems, and fast growing population. Therefore, flows in the dam reservoir using proper realistic water modeling should be well defined. In this study, three-dimensional hydrodynamic simulation model of an actual dam reservoir for a season is created. The density differences between inflow river and ambient dam reservoir water can create stratified and circulation flows in the real dam reservoirs. The density differences can be due to the discrepancies in temperatures, concentration of dissolved or suspended substances, or a combination of both. The numerical model is developed using nonlinear and unsteady continuity, momentum, energy, and k-e turbulence model equations. In order to include the Coriolis force effect on the flow in a dam reservoir, Coriolis force parameter is also added to the model equations. These equations are constructed using actual dimensions, shape, boundary, and initial conditions of the dam and reservoir. The 3-D mathematical model developed is capable of simulating the flow and thermal characteristics of the reservoir for using season. Model simulations results are compared with field measurements obtained from gauging stations. The results are found to be in accordance with the field measurements.

Determination of Nearshore Sandbar Crest Depth Using Neural Network Approach

For the coastal structure designs, nearshore sandbars are crucial since they are affected highly from various parameters like beach slope, the height and period of the wave and the properties of the material forming the bed. In this study, it was investigated the sediment movements in nearshore by using various bar crest depths and a physical model. Erosion profile output is used for determination of the bar crest depths. Linear and non-linear regression methods are used for obtaining the non-dimensional equations with the experimental data. These equations are then compared with the ones found in the literature. Transportation of on-off shore sediments is affected by bar crest depth which has been examined with the materials forming the beach by using various diameter of the medium as d50=0.25, 0.32, 0.45, 0.62 and 0.80 mm. In order to estimate nearshore sandbar crest depth, we have developed an approach by using neural network (ANN). For proposing the efficiency of the study, ANN and multi-nonlinear regression models are compared with each other.

Suspended Sediment Estimation Using an Artificial Intelligence Approach

Forecasting of sediment concentration in rivers is a very important process for water resources assignment development and management. In this paper, a neural network approach is proposed to predict suspended sediment concentration from streamflow. A comparison was performed between artificial neural network, sediment rating-curve and multilinear regression models. It was based on a 5 years period of continuous streamflow, suspended sediment concentration and mean water temperature data of West Virginia, Little Coal River, Danville station operated by the United States Geological Survey. Based on comparison of the results, it is found that the artificial neural network model gives better estimates than the sediment rating-curve and multilinear regression techniques.

THE ESTIMATION OF THE AMOUNT OF THE MONTHLY VAPORATION AT THE DAM OF TAHTAKÖPRÜ WITH THE APPROACH OF THE ARTIFICIAL NEURAL NETWORKS

In this study the estimation of the amount of the monthly evaporation is investigated by the method of Articifial Neural Networks (ANN). According to these results; the analytical results of ANN model have given a better approach as compared to the classical methods that are used in the past on the amount of the monthly evaporation of the dams reservoir.