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Browsing by Author "Unluturk, A."

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    Comparison of PSO-LQR and PSO-PID Controller Performances on a Real Quarter Vehicle Suspension
    (Institute of Electrical and Electronics Engineers Inc., 2023) Akgul, T.; Unluturk, A.
    Active Suspension Systems (ASS) are significant vehicle mechanisms that impact both driving safety and comfort through the utilization of actuators incorporated within their structure. In this study, Particle Swarm optimization (PSO) based Linear Quadratic Regulator (PSO-LQR) and Proportional Integral Derivative (PSO-PID) controllers are designed and implemented on an ASS. A weighted objective function is defined for the PSO algorithm, encompassing crucial performance criteria of the ASS, such as vehicle body acceleration, suspension movement, and tire deflection. The controller structures tested on the ASS are compared with Passive Suspension Systems (PSS), considering the performance criteria. The obtained results reveal that the PSO-LQR controller significantly improves the vehicle body acceleration, suspension travel, tire deflection and dynamic load on the tire compared to the PSS and PSO-PID controllers. © 2023 IEEE.
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    Image Processing-Based Assessment of Dust Accumulation on Photovoltaic Modules
    (Institute of Electrical and Electronics Engineers Inc., 2019) Unluturk, M.; Kulaksiz, A.A.; Unluturk, A.
    Numerous environmental factors significantly affect the energy yield of solar photovoltaic (PV) power plants. Among these, solar irradiance, photovoltaic module temperature, dust and shading are prominent. The level of soiling is directly related to the installation site of the PV plant. In this study, to investigate the impact of dust shading factor on energy efficiency, artificial light source in laboratory environment is used and power outputs are compared for three different densities of dust accumulation on the module surface. For each level of dust accumulation, images are obtained from PV modules. From the PV module images obtained by a camera for different levels of dust accumulation, new features are obtained based on Gray Level Co-occurrence Matrix. The obtained data with new features are classified on the basis of Artificial Neural Networks to determine dust level and its effect on PV module performance. © 2019 IEEE.