Yazar "Eminoglu, U" seçeneğine göre listele

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    Application of SVC on dynamic load for different load types
    (UNIV WEST ENGLAND-UWE, 2005) Eminoglu, U; Asun, SA; Alcinoz, T
    This paper investigates the effect of different characteristics of dynamic load on the system voltage profile and active/reactive power variations. For the dynamic load, exponential recovery load model is used and four different exponents of the load are considered to model different dynamic loads. For the voltage control, Static Var Compensator (SVC) is implemented and dynamic analysis of SVC on voltage control is simulated with a simple-three bus power system using Matlab SimPowerSystems Blocksets. The simulation results indicate that significant improvement on voltage profile could be achieved by using SCVs for different dynamic loads.
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    Location of facts devices on power system for voltage control
    (UNIV WEST ENGLAND-UWE, 2005) Eminoglu, U; Alcinoz, T; Herdem, S
    The modelling of the load has a significant effect in the electrical power systems. This paper presents the effect of different static load models on the location of Static VAr Compensator (SVC). The static load types, in which active and reactive powers vary with voltage as an exponential form, are used. The effect of appropriate location of the SVC on voltage control for variable load conditions is investigated. For this purpose each load is varied as a stair-case and voltages are controlled at the desired levels by using minimum number of Static Var Compensator (SVC). Modelling and simulation of the system are performed using Matlab SimPowerSystems Blocksets. PI controllers are used to control SVC firing angles. The studied power system is a simple five-bus system.
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    Short term and medium term power distribution load forecasting by neural networks
    (PERGAMON-ELSEVIER SCIENCE LTD, 2005) Yalcinoz, T; Eminoglu, U
    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey. (C) 2004 Elsevier Ltd. All rights reserved.