H. Ibrahim Bulbul

Position Control of a DC Motor Used in Solar Panels with Artificial Neural Network

In this study, it is aimed to model a dc motor which will be able to achieve panel movement in solar panels and realize position control using artificial neural networks method in LabView. Position control of DC motors is possible with position sensors integrated to motors or external position sensors. The most popular of these sensors are encoder and transducer. Within this study, by using the LabView program in the computer environment, DC motor model was created and simulations were done on this model. In simulations, the motor current, position and velocity were measured from motor model. The Current and position data collected from simulations was used as the closed-loop feedback control system. As the control system, artificial neural network (ANN) was used due to the nonlinear structure of the current data.

Multi-time series and -time scale modeling for wind speed and wind power forecasting part I: Statistical methods, very short-term and short-term applications

This study concentrates on multi-time series and - time scale modeling in wind speed and wind power forecasting. Different statistical models along with different time horizons are analyzed and evaluated broadly and comprehensively. For this reason, the entire study is divided into two main scientific parts. In case of making a general overview of the entire study, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) methods are employed for multi-time series modeling. Very short-term, short-term, medium-term and long-term scales are utilized for multi-time scale modeling. Specifically, in this part of the entire study, the mentioned statistical models are presented in detail and 10-min and 1-h time series forecasting models are created for the purpose of achieving 10-min and 2-h ahead forecasting, respectively. Many useful outcomes are accomplished for very short-term and short-term wind speed and wind power forecasting.

Multi-time series and -time scale modeling for wind speed and wind power forecasting part II: Medium-term and long-term applications

This paper represents the second part of an entire study which focuses on multi-time series and -time scale modeling in wind speed and wind power forecasting. In the first part of the entire study [1], firstly, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are introduced in-depth. Afterwards, the mentioned models are analyzed for very short-term and short-term forecasting scales, comprehensively. In this second part of the entire study, we address the medium-term and long-term prediction performance of MA, WMA, ARMA and ARIMA models in wind speed and wind power forecasting. Particularly, 3-h and 6-h time series forecasting models are constructed in order to carry out 9-h and 24-h ahead forecasting, respectively. Many valuable assessments are made for the employed statistical models in terms of medium-term and long-terms forecasting scales. Finally, many valuable achievements are discussed considering a detailed comparison chart of the entire study.

Implementation of unit commitment algorithm: A comprehensive droop control technique to retain microgrid stability

Though natural droop control technique is a familiar technique to control the voltage and frequency droop in microgrid application, due to some limitations to handle different cases of application, at present time, it is not appreciable to the scientists and researchers world-wide and virtual droop control technique is developing into the most prominent control technique to regulate the frequency and voltage droop in smart grid system. In this paper, two distinct unit commitment strategies, one for islanded operation and another for grid-tied operation, are proposed for the natural gas generators backed microgrid arrangement to enhance the overall efficiency of the system and significantly scale down the fuel consumption. These novel control frameworks offer revamped implementation of renewable energy resources and minimization of the natural gas generator by using a unit commitment algorithm. Besides that, here, the virtual droop control is demonstrated for the grid tied operation of the microgrid arrangement. Furthermore, all the necessary algorithms are illustrated with proper schematics and the results are verified by Matlab simulations.

System identification and control of an wound rotor AC induction machine for wind turbine

In this study a wound rotor AC induction machine which can be used in wind turbine is modeled in LabVIEW program and the system model optimization is done with system identification (SI) method. Wind turbines are usually used as synchronous and asynchronous. In this study, to be safe and because of the advantages such as low costs, WRIM is preferred. In the control of the excitation circuit, SI method is used to provide more safe, effective and efficient control. With this method, control parameters are optimized to be used on the system. In simulations real wind data are used as input to approach reality.

An analytical analysis of Turkish digital forensics

Abstract The first glimpses of digital forensics (DF) starts back in 1970s, mainly financial frauds, with the widespread use of computers. The evolution of information technologies and their wider use made the digital forensics evolve and flourish. Digital forensics passed a short but complex way of “Ad-Hoc”, “Structured” and “Enterprise” phases nearly in four decades. The national readiness of countries might vary for those phases depending on the economy, legislation, adoption level, expertise and other factors. Today digital forensics discipline is one of the major issues of law enforcement (LE), government, defense, industry, academics, justice and other non-governmental organizations as stakeholders have to deal with. We wanted to assess the maturity level of “Turkish Digital Forensics” in view of the digital forensics historical phases, along with some specific institutional & organizational digital forensics issues. The current digital forensic capacity and ability, understanding and adoption level of the discipline, education and training forecasts, current organizational digital forensics framework and infrastructure, expertise, certification and knowledge gained/needed by digital forensics community, tools and SW-HW used in digital forensics, national legislation, policy making and standardization issues along with the anticipated requirements for near future are aimed to address by an online survey. This paper discusses the aforementioned national issues with respect to the digital forensics discipline. It does not examine all aspects of digital forensics. The general assessment we had reached for the maturity level of “National DF” is in between the structured and enterprise phases, with a long way to go but with promising developments.

Statistical Scenarios for Demand Forecast of a High Voltage Feeder: A Comparative Study

The electricity demand forecasting has gained remarkable concern in energy market operation and planning with the emergence of deregulation in the power industry. Power system operators benefit from accurate demand forecasts by supporting investment decisions more objectively. As a crucial requirement, this paper focuses on hourly demand forecasts of a high voltage feeder. Moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models have been used for creating statistical demand scenarios at 1-h, 2-h, 3-h and 4-h intervals. Many constructive comparisons have been conducted among MA, WMA, ARMA and ARIMA models comprehensively. Besides, the best statistical model employed in each hourly demand scenario provides the robust improvement percentage with respect to the persistence model.

Optimization of operating conditions of photovoltaic systems: A case study

Nowadays, there are several difficulties are met in the selection of appropriate conditions, place of use, and purpose of photovoltaic (PV) systems. The reason is that electrical power output of PV system shows a non-linear variation, unlike other applications, depending on the internal resistance of electrical devices (loads) as well as radiation intensity and environmental temperature. Therefore, electrical characteristics of PV panel-load must be evaluated together for correct optimization. In this study, running an electrical device having optimized curve of linear load on a monthly basis of a panel was analyzed. At the same time, surface temperature of the panel, panel inclination angle and value of the load that concerning electrical load impact on power output of PV panels was examined experimentally and theoretically.

Modeling a permanent magnet synchronous generator used in wind turbine and the realization of voltage control on the model with artificial neural networks

In this study, the LabVIEW modeling of a permanent magnet synchronous generator that will be used in wind turbine and the voltage control with artificial neural networks on the model are done. Typically synchronous, asynchronous and direct current generators are used in wind turbines. In the scope of this study, synchronous generators are preferred due to the advantages such as high efficiency and high reliability. the artificial neural networks are used in the control of excitation circuit in taking into account the dynamic nature of the wind.