Browsing by Author "Ekiz, H"

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    Designing a Real-time Remote Control System for Undergraduate Engineering and Engineering Technology Students
    Yabanova, I; Taskin, S; Ekiz, H; Oguz, Y; Akaslan, D; Yumurtaci, M
    This paper explains the design of a control system intended to enhance the quality of education in engineering and engineering technology departments and considers the use of various concepts: remote-access, reconfigurable properties, and multi-user controls. Controlling the system's hardware is possible by using fuzzy logic and PID control methods to teach several engineering concepts in the field of automatic control (i.e., DC motor and temperature control). MD parameters and fuzzy logic membership rules can be modified by authorizing remote users. A Web interface was also designed as part of the system to create an opportunity for the end-users to observe real-time changes in DC motor and temperature control. Modifications to the control system's parameters also enable users to record output to their own personal computers via the Internet; this allows users from. other engineering institutions to access and modify the PID and fuzzy logic membership rules for more experiences. In ibis way, various theoretical and practical concepts associated with the field of automatic control can be implemented without any limitations. The overall findings from this study indicate that engineering and engineering technology curricula in any institution can effectively be delivered using the Internet to overcome the limitations of distance, as remote-access experiments are becoming highly applicable anywhere and anytime.
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    A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions
    Gokozan, H; Taskin, S; Seker, S; Ekiz, H
    This study presents an artificial neural network based intelligent monitoring algorithm to detect of a power system harmonics. The proposed approach was tested on the current and voltage data of an induction furnace power system, which was collected by using a LabVIEW based measurement system under different load conditions. The collected data was analyzed with MATLAB program using the short-time Fourier transform and power spectral density estimation methods to find the dominant harmonics of the power system. After determination of the operational region in terms of the harmonic levels under the full-load condition, the power spectral density algorithm was used for determining the stationary intervals of the power system data. Identified harmonic frequencies were used to train the artificial neural network algorithm, which was then tested for harmonic estimation at different load conditions. Hence, the neural network topology was used as a artificial follower. Results demonstrate that harmonic state estimation of a power system can be achieved by an error variation at the output of the auto-associative neural network.