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    Öğe
    A daily multiobjective optimization model in smart grids
    (Ieee, 2016) Daylak, Funda; Ceylan, Oğuzhan; Karatekin, Canan Zobi
    This paper solves a daily multiobjective optimization model for efficient operation of power distribution systems. The model aims to minimize the voltage deviations, minimize power losses and minimize the energy costs of the distributed generators. The model is implemented on a modified 33 bus distribution system by including: two voltage regulators and four distributed generators. Three different simulation sets are performed: the first one includes only voltage regulators and the second one includes only distributed generators and the third one includes both voltage regulators and distributed generators. When the simulation results are compared, results show that better voltage profiles with less power losses and costs might be obtained by using both voltage regulators and distributed generators.
  • Küçük Resim
    Öğe
    Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design
    (MDPI, 2025) Daylak, Funda; Özoğuz, Serdar
    This study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit configurations, was used to train and validate the model, providing an accurate representation of the entire dataset without requiring full-scale data analysis. In forward modeling, the same subset was used to train the network, which was then used to predict DR values for the remaining dataset. This approach enabled the identification of circuit parameters that resulted in optimal DR values. The results confirm the effectiveness of these techniques, with both inverse modeling and forward modeling outperforming the standard circuit design. At 160 kHz, a critical frequency for the operation of the designed filter, inverse modeling achieved a DR of 140.267 dB and forward modeling reached 136.965 dB, compared to 132.748 dB for the standard circuit designed using the traditional approach. These findings demonstrate that ANN-based methods can significantly enhance design accuracy, reduce time requirements, and improve energy efficiency in analog circuit optimization.