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    Analysis of Induction Motor-pump System Supplied by a Photovoltaic Generator for Agricultural Irrigation in Southeastern Anatolian Region of Turkey
    (Springer Singapore Pte Ltd, 2015) Gumus, Bilal; Yakut, Yurdagul Bentesen
    In agricultural systems, significant amount of energy is consumed during irrigation periods. Therefore operating irrigation systems with electrical energy produced by solar energy is very important. It is be possible to operate irrigation systems which have small-pump power like drip-irrigation with electrical energy produced by solar energy. Electrical energy produced by photovoltaic panels can vary from the estimated value due to environmental factors. Consequently analysis of a real system's performance is important. Thus, more correct projections can be made for the systems which will be designed. In this study, induction motor-pump mechanism for drip-irrigation system is operated with photovoltaic generator. Solar energy capacity of the established system is evaluated by measurements in irrigation periods. By means of simulations, power values produced by system and gained from the actual system are compared. Additionally the performance of induction motor is analyzed with the help of the driver system that increases the efficiency and controls the motor. As regards of results, design values of the drip-irrigation systems fed with solar energy in Southeastern Anatolian Regions of Turkey are obtained. Performance results of induction motor controlled with driver are also provided.
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    Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets
    (Mdpi, 2024) Turanli, Ozan; Yakut, Yurdagul Bentesen
    Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system's protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.
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    Comparation of PI and Neural Fuzzy Based Closed Loop Control Methods for Permanent Magnet Synchronous Motor Fed by Matrix Converter
    (Ieee, 2015) Yakut, Yurdagul Bentesen; Sunter, Sedat; Ozdemir, Mehmet
    Permanent magnet synchronous machines are used in many industrial applications today. In particular, depending on the developments in magnetic materials, permanent magnet synchronous motors rapidly developing in recent years have found themselves a widespread coverage in applications because of their outstanding features. When the research in the literature is surveyed, it is observed that the vector control of electrical machines is done by means of various methods. One of the most common applications of artificial intelligence used in the analysis of electrical machines is Neuro-Fuzzy, a combination of Fuzzy Logic and Neural Network. In this paper, the vector control of PMSM fed by a matrix converter is modeled and simulated by using PI controllers and Neuro-Fuzzy, one of the intelligent methods. Waveforms derived from the simulation of the vector control of PMSM fed by a matrix converter were examined comparatively for PI and Neuro-fuzzy controllers.
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    A CONTROL METHOD FOR DRIVING DUAL PERMANENT MAGNET SYNCHRONOUS MOTORS FED BY SINGLE MATRIX CONVERTER
    (Univ Osijek, Tech Fac, 2017) Yakut, Yurdagul Bentesen; Sunter, Sedat; Ozdemir, Mehmet
    In this study, a control method is created to drive two parallel-connected permanent magnet synchronous motors fed by a single converter. Matrix converter used in this work provides ac-ac conversion in one stage. The matrix converter model and d-q model of dual PMSMs are performed in Matlab/Simulink. In industrial applications containing dual motors fed from a single converter, volume and weight of the drive system decreases, power electronics switches and other components are reduced and the installation cost decreases. Because of these advantages, many methods are being developed by researchers to control the dual motor drives. In this study, the motor control has been performed by using the average speed method. Control of dual permanent magnet synchronous motors with a single converter is implemented with ANFIS-based neural fuzzy controllers. All parameters of the motors are taken equal since two identical permanent magnet synchronous motors are considered. Two various operation conditions are investigated in the simulation. In the first case, one of the identical motors is operated on no-load, while the other one is operated with the rated load. In the second case, each motor is operated with various load torques varying with the time. Corresponding simulation results have been presented to show the performance of the drive system.