Yazar "Balci, Selami" seçeneğine göre listele

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    The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications
    (Springer Heidelberg, 2022) Unlersen, Muhammed Fahri; Balci, Selami; Aslan, Muhammet Fatih; Sabanci, Kadir
    In this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, and motor power). The rotor speed is defined as the output parameter to determine the dynamic response, and 600 parametric data are obtained according to the simulation studies. In order to estimate the rotor speed of the BLDC motor modeled using artificial intelligence (AI), an advanced recurrent neural network architecture known as bidirectional long short-term memory has been designed. Rotor speed is successfully estimated with the proposed architecture, and as a result, the mean absolute percentage error value is calculated as 3.25%. These results show that the analysis of BLDC motor parameters can be determined quickly with the proposed AI method without long-running simulations.
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    Thermal behavior estimation of the power switches with an empirical formulation optimized by Artificial Bee Colony algorithm
    (Pergamon-Elsevier Science Ltd, 2021) Unlersen, Muhammed Fahri; Balci, Selami; Sabanci, Kadir
    Temperature rise and thermal management in power electronic circuits is a very important issue both electrically and in terms of more compact circuit design. Considering the non-linear temperature rise in the power switches, it is necessary to accurately estimate the temperature rise before the circuit can be experimentally executed. In this study, the temperature rise values in the switching elements for the different current, switching frequency, and duty ratio values of a DC-DC boost converter circuit are analyzed parametrically with ANSYS-Twin Builder software and a total of 715 data are obtained. Based on these data obtained, the temperature rise that occurs during the operation of the power switches in different parameters can be predicted and can be predicted accurately and quickly with a mathematical expression in order to effectively provide the cooling system and thermal management of the power electronics circuit. The proposed expression has eight parameters that need to be optimized. In optimization of these parameters, the Artificial Bee Colony algorithm is used. The Mean Absolute Error is used as the performance indicator. The temperature rise values are calculated via the optimized expression with 0.7446 degrees C error. Thus, the accuracy of expression is about 95.17%.