Yazar "Çam E." seçeneğine göre listele

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    A fuzzy logic controller application for thermal power plants
    (2006) Kocaarslan I.; Çam E.; Tiryaki H.
    This study presents a fuzzy logic based control technique to regulate the power and enthalpy outputs in a boiler of a 765 MW coal fired thermal power plant. An approximate mathematical model of the thermal power plant was developed by using real time data on Computer Aided Design and Control (CADACS) software. Conventional proportional, integral and derivative (PID), fuzzy logic (FL) and fuzzy gain scheduled proportional and integral (FGPI) controllers have been applied to the power plant model. The simulation results show that the FGPI controller developed in this study performs better than the rest of the controllers on the settling time and overshoot of power and enthalpy outputs. © 2005 Elsevier Ltd. All rights reserved.
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    Öğe
    Load frequency control in four-area power systems using PID controller
    (2010) Yalçin E.; Çam E.; Lüy M.
    In this paper, load frequency control (LFC) which is one of the most important issues in interconnected power systems is iffectuated by using PID controller which is used in many sectors, and also investigated its performance. Four-area power system is prefered for LFC problem in multi area power systems are more complex and important than one area power system. In this paper, LFC problem is defined firstly, and then mathematical models of power systems are explained. Finally PID controller are described shortly and power system is modelled by using MATLAB-Simulink platform.
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    Öğe
    Prediction of wind speed and power in the Central Anatolian region of Turkey by adaptive neuro-fuzzy inference systems (ANFIS)
    (2006) Çam E.; Yildiz O.
    An adaptive neuro-fuzzy inference systems (ANFIS) model was used for predicting regional average wind speed and power values in the Central Anatolian region of Turkey. In model development, longitude, latitude and altitude of wind stations and wind speed measurement height were taken as input variables, while wind speed and power values were taken as output variables for 4 different surface roughness characteristics. After a successful learning and training process the proposed model produced reasonable mean errors ranging from 0.19% to 2.89% and negligible root mean square errors in training and testing wind speed and wind power data. Overall, the study results suggest that the ANFIS model can be used as an effective tool to estimate average wind speed and power values in the study area. © TÜBİTAK.