Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026/r/gumushane/13769
  Enhanced Sensorless Current Control in Boost Converters for EV Fast Charging: Equilibrium Optimization-Tuned Cascaded ANN-Fuzzy Logic Control

  Altan, Aytac; Yanarates, Cagfer; Alzaidi, Mohammed S.

  Efficient current control in boost converters is critical for electric vehicle (EV) battery charging applications, where dynamic operating conditions challenge conventional control strategies. This paper presents a novel cascaded artificial neural network (ANN) and fuzzy logic (ANFIS) controller designed to enhance current regulation in boost converters. The performance of the proposed approach is validated through comparison with single PI, cascaded PI, and single ANFIS control schemes. To improve system performance, a sensorless current estimation technique is implemented, reducing hardware dependency and enhancing robustness, while lowering system cost. Experimental evaluations demonstrate that the proposed cascaded ANFIS controller maintains consistent current levels under rapid load changes, providing improved transient response and steady-state accuracy. Specifically, the proposed method achieves a settling time of 23 ms, compared to 185 ms for single PI and 66 ms for single ANFIS control, corresponding to an 87.6% improvement over PI. In addition, overshoot is reduced from 37.25% (PI) and approximately 20% (ANFIS) to negligible levels, while a fast rise time of 3.62 ms is obtained. These improvements reduce current deviation and transient stress, contributing to more stable and reliable charging operation. Although charging time, efficiency, and battery lifetime are not directly measured, the results indicate strong potential for improved practical charging performance. The proposed approach provides valuable insights into the integration of intelligent control methods in power electronics and supports the development of high-performance EV charging systems.

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  İncele
• MakaleAçık Erişim2025/r/gumushane/14149
  Solving the Dynamic Stability Challenge of Automatic Gain-Controlled Phase-Locked Loops: A Robust Alternative via Enhanced SRF-PLL Design and Implementation

  Yanarateş, Cağfer; Altan, Aytaç

  This study presents a robust alternative to conventional phase-locked loop (PLL) architectures through the design and real-time implementation of an enhanced Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) for single-phase inverter systems. Targeting the inherent dynamic stability limitations of Automatic Gain-Controlled PLLs (AGC-PLLs), the proposed SRF-PLL demonstrates superior performance in transient response and tracking accuracy. Comparative evaluations under diverse dynamic operating conditions—including phase angle jumps, voltage sags/swells, frequency steps, noise, and harmonic distortion—highlight its resilience and adaptability. Experimental results reveal a substantial improvement in dynamic response, achieving 2.5 to 6 times faster settling times depending on the operating condition. Moreover, peak-to-peak oscillations were significantly suppressed, with deviations reduced from 2° to nearly 0° under a 5 Hz frequency step scenario, even in the presence of 3rd order harmonic injection. For a 40° phase angle step, the proposed system achieves a fivefold improvement in attenuation time (0.03 s vs. 0.11 s) compared to the AGC-PLL. Under voltage sag conditions with noise and 3rd order harmonic injection, the enhanced SRF-PLL reduces the attenuation time from 0.2 s to 0.08 s. During voltage swell scenarios, it further improves the response time from 0.025 s to 0.01 s. These findings confirm the practical benefits and efficiency of the enhanced SRF-PLL for applications requiring precise and reliable phase tracking and synchronization.

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• MakaleAçık Erişim2024/r/gumushane/12613
  Enhanced Hysteresis Droop Control for Equivalent Load Sharing in Parallel PWM DC-DC Converters: Optimized Slew Rate by Resonant Filtering

  Yanarates, Cagfer; Altan, Aytac

  In this study, enhanced hysteresis droop control for equivalent and fast load sharing in parallel PWM DC-DC converters is presented. The basis behind the work encompasses power system stability, reliability, efficiency, reduction of losses and decrease in the operational cost. In this context, phase management, which constitutes the biggest problem, and the resulting factors such as ripple cancellation, thermal stress, transient response behavior and complexity, is aimed to manage with the proposed control scheme. Performance evaluation of the proposed system is based on sensitivity analysis against possible disturbance and noise, and comparative analysis with PI controller against uncertainty and parameter variations using mathematical modeling and simulation techniques. The results show that the proposed controller has improved performance in both noise attenuation and disturbance rejection at typical switching frequencies of DC-DC converters ranging from 100 kHz to a few MHz. Additionally, applying the proposed control technique considering 10% parameter variations led to the largest difference being recorded at 4% while this difference was 18.68% in conventional PI control. According to the findings of the simulation, the proposed control scheme reduces the settling time by a factor of 4.89, whereas it takes 775.862 μs seconds with the PI controller. Furthermore, with the suggested controller, the overshoot - which was 36.09% with the PI controller - is reduced to just 1%.

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  İncele
• MakaleAçık Erişim2024/r/gumushane/11788
  Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning

  Gökgöz, Baki; Aydin, Tolga; Gül, Fatih

  The traditional Von Neumann computing architecture, which necessitates data transfer between external memory and the processor, incurs significant energy and time costs when running deep learning (DL) and machine learning (ML) architectures. The primary issue with the energy and time efficiency of this architecture stems from the frequent and intensive data transfers between memory and the processor. Therefore, memristive synaptic devices are utilized to overcome this energy and time inefficiency while performing cognitive tasks. The fundamental working principle of memristive devices is to reduce the need for data transfer by combining memory and processing in the same location, thereby significantly decreasing both energy consumption and the time required for operations. However, to achieve the desired level of efficiency in terms of energy and time consumption from neuromorphic systems, the performance of these systems needs to be further improved with respect to accuracy and test error rates for classification applications. Achieving high accuracy performance in such deep learning or machine learning models necessitates optimization processes not only at the hardware level but also at the algorithmic level. In this context, this paper presents a comprehensive examination and comparison of the frequently used SGD and its momentum variants for deep learning and machine learning applications in memristor-based neuromorphic computing systems. The study thoroughly investigates the performance of critical metrics such as the learning properties, energy efficiency, and accuracy rates of the nano-scale titanium dioxide (TiO2) based synaptic device. The experimental results for the MNIST dataset showed AdaDelta 89.48%, AdaGrad 79.00%, Adam 79.13%, AdaMax 79.68%, Momentum 88.55%, Nadam 81.20%, RMSprop 84.91% and SGD 89.47% accuracy. The experimental results for the CIFAR dataset showed AdaDelta 90.51%, AdaGrad 82.08%, Adam 83.10%, AdaMax 81.76%, Momentum 91.25%, Nadam 82.45%, RMSprop 88.11% and SGD 90.21% accuracy. 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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• MakaleAçık Erişim2024/r/gumushane/11467
  Optical Filter Design for Multi-Color VLC in Reflective Environments with Multiple Lighting Sources

  Kucuk, Mehmet A.; Albayrak, Cenk; Turk, Kadir; Yanikomeroglu, Halim

  Multi-color visible light communication (MC-VLC) systems, which aim to transmit data in parallel with multi-color LEDs, use optical filters to separate color channels at the receiver side. However, the relatively wide spectrum of LEDs may cause inter-color interference (ICI). In addition, the passband of the optical filters shifts towards the blue wavelength depending on the angle of incidence (AoI) of the light, which may also increase ICI. In this paper, the problem of designing optical filters for MC-VLC systems in the presence of the blue-shift is investigated. The communication environment is assumed to be a typical room illuminated by multiple fixtures in accordance with lighting standards. Moreover, the walls in the room are reflective, resulting in non-line-of-sight (NLoS) components. The passband edges of the optical filter are optimized to maximize the average signal-to-interference-plus-noise ratio (SINR). The effect of the field-of-view (FOV) of the receiver on the system performance is also analyzed. Finally, a generic optical filter has been designed that can be used in standards-compliant environments for selected LEDs and a given FOV range. The results show that the designed optical filter provides a higher and uniform capacity than those in the literature.

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