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Öğe A Fuzzy Logic-Based Energy Management Approach for Fuel Cell and Photovoltaic Powered Electric Vehicle Charging Station in DC Microgrid Operations(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Cakmak, Recep; Bayrak, Gokay; Koc, MehmetHydrogen and electric vehicles (EVs) stand out as two promising technologies with the potential to revolutionize the future of transportation. The adoption of hydrogen and EVs indicates a noteworthy shift in the transportation paradigm, offering the prospect of reshaping this sector. This paper introduces an energy management approach based on fuzzy logic for a charging station that combines fuel cell (FC) and photovoltaic (PV) to power electric vehicles (EVs). The study emphasizes developing and simulating a fuzzy logic-based energy management system tailored for DC microgrid operations, including on-grid and islanded microgrid schemes. The system is designed and simulated in a digital simulation environment. The findings indicate that the fuzzy logic approach significantly enhances the performance of PV and FC-based EV charging stations. This research contributes to developing sustainable and efficient solutions for integrating electric vehicles and hydrogen-powered supply systems.Öğe A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station(Pergamon-Elsevier Science Ltd, 2023) Bayrak, Gokay; Yilmaz, Alper; Cakmak, RecepThis study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary condi-tions in threshold value methods are avoided and at the same time, cost and computa-tional burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20-40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe A new intelligent charging strategy in a stationary hydrogen energy-based power plant for optimal demand side management of plug-in EVs(Pergamon-Elsevier Science Ltd, 2024) Cakmak, Recep; Meral, Hasan; Bayrak, GokayStationary hydrogen energy-based power plants generating electricity to supply high-powered plug-in electric vehicles (PEVs) have recently become popular in renewable energy-based power plants. Besides, in a PEV charging station, various types of powered charge devices can be established such as DC fast chargers or 3.7 kW, 7.4 kW, 11 kW, and 22 kW AC chargers. This paper introduces a demand-side management-oriented optimal charging strategy that includes two stages for PEVs in a hydrogen energy-based microgrid. The paper focuses on two stages to execute an optimal charging of PEVs in compliance with their users' requests and satisfaction and considering the power system loading. It is assumed that there are three types of chargers in the PEV charging station and the users. In the first stage randomly created requests are classified by an ensemble learning classifier method that performs higher performance classification by combining the results from multiple classifiers in a machine learning classification. The second stage schedules the PEVs according to the classification results and users' requests. To test the proposed system, first random requests are created then they are sent to the classifier, and the results of classifiers are scheduled in each other. The demand-side management-oriented charge scheduling and managing strategy which includes the proposed two stages has been compared with nonmanaged cases. Case study results reveal that the proposed approach provides 52.1% peak load reduction and 72.3% valley filling improvement by the SOS algorithm. The results highlight the advantages of the proposed system in terms of peak reduction and valley filling.Öğe Elektrikli Araç Şarj İstasyonunun Şebeke Ile Entegrasyonu Için Adaptif Eşik Değer Yaklaşımlı Dalgacık Dönüşümü-Tabanlı Gerçek Zamanlı Güç Kalitesi Tespit Yönteminin Geliştirilmesi(2022) Bayrak, Associate Professor Gökay; Yılmaz, Alper; Cakmak, Recep; Öner, SametBu projede, dağıtık generatörler tarafından beslenen elektrikli araç şarj istasyonlarında güç kalitesi olaylarını (GKO) tespit etmek ve sınıflandırmak için örnek indirgenmemiş dalgacık dönüşümü (ÖİDD) ve bulanık karar verici (BKV) kullanan hibrit bir yöntem sunulmaktadır. Önerilen adaptif ÖİDD-BKV, gürültü içeren sinyallerde eşik değer-tabanlı sinyal analiz yöntemlerinin performans eksikliği giderilmiştir. Ayrıca, önerilen yöntem şebeke sinyali gibi gürültü içeren sinyallerde gürültü bastırımı için uygulanan filtreleme işlemine olan ihtiyacı ortadan kaldırmıştır. Bu adaptif yaklaşım ile eşik değer yöntemlerinde sınır koşullarında tespit hataları önlenirken aynı zamanda gerilim sinyalinin detay katsayılarında sadece tepe değerleri kullanılarak maliyet ve hesaplama yükü en aza indirilmiştir. Gürültüsüz koşullarda önerilen yöntem için ortalama test doğruluğu %96,13'tür. Ayrıca ÖİDD-BKV, sinyal gürültü oranı (SGO) değeri 20-40 dB olan yüksek seviyeli gürültü koşullarda ortalama %94,96 sınıflandırma doğruluğuna sahiptir. ÖİDD-BKV yönteminin etkinliği bir deney düzeneği kullanılarak test edilmiştir. Deneysel veriler için ortalama test doğruluğu ise %96,66'dır.
