Yazar "Daylak, Funda" seçeneğine göre listele

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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.
  • Küçük Resim
    Öğe
    Neural network based frequency adaptive digital predistortion of RF power amplifiers
    (Springer, 2025) Daylak, Funda; Özoğuz, Serdar; Kouhalvandi, Lida; Bayat, Oğuz
    Linearization of power amplifiers (PAs) is a big challenge in high-dimensional radio frequency (RF) designs, and to tackle this drawback we propose an adaptive strategy with the combination of neural networks (NNs) and band-pass filters for input signals with different frequencies that results in reduced computational costs. The proposed linearization approach is based on utilization of NN for modeling the PA and band-pass filters for contributing to frequency adaptability without feedback loop. Thus, even if the frequency of the input signal changes, the system may still produce linear output. The proposed model consists of sub-digital predistortion (DPD) blocks where each sub-DPD block generates DPD coefficients only for the specified frequency range. Thanks to sub-DPD blocks without feedback, the computational load of the model is reduced and computation time is saved. To validate the proposed model, the PA is first characterized using the neural network. Then, the frequency of the input signal is determined via band-pass filtering. Based on this frequency information, the corresponding NN-based sub-DPD block is activated to linearize the PA’s nonlinear behavior. For the presented PA that is operating from 1.7 GHz to 2 GHz, four different input signal frequencies values as 1.7 GHz, 1.9 GHz, 2.1 GHz, 2.4 GHz respectively are carried out. The achieved results prove that the proposed model provides improved PA modeling and nonlinear compensation compared to the other methods. The 1-dB compression point of the PA is measured as–6.88 dBm without DPD, 4.49 dBm with look-up table-based DPD, and 7 dBm with NN-based DPD.