Kaya, Hüseyin

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https://hdl.handle.net/20.500.12469/6885
Name Variants
Kaya, Hüseyin
H.,Kaya
H. Kaya
Hüseyin, Kaya
Kaya, Huseyin
H.,Kaya
H. Kaya
Huseyin, Kaya
Kahya, Huseyin
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Dr. Öğr. Üyesi
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Management Information Systems
Management Information Systems
03. Faculty of Economics, Administrative and Social Sciences
01. Kadir Has University
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Former Staff
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2

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19th International Conference on Compatibility Power Electronics and Power Engineering-CPE-POWERENG-Annual -- MAY 20-22, 2025 -- Antalya, TURKIYE1
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  • Thumbnail Image
    Master Thesis
    Bir Kuresel Guc Olarak Avrupa Birligi
    (Kadir Has Üniversitesi, 2008) Kahya, Huseyin; Ozgoker, Ugur
    Soguk Savas sonrasi donemde Dunya'da yasanan degisim Avrupa Birligi'ni de etkilemistir. Bu doneme kadar ekonomik butunlesmeyle sinirli kalan Avrupa Birligi bu tarihten sonra Avrupa'da degisen siyasi durum ekonomik butunlesmenin ust seviyeye ulasmasi gibi bir takim nedenlerden dolayi siyasi ve askeri alanlarinda da butunlesme icine girmistir. Avrupa Birligi 90'li yillardaki etnik ve dini catismalarda ve irak Savasinda Birligin durusu siyasi ve askeri acidan iyi bir sinav vermemistir. Bu calismada Avrupa Birligi'nin Dunya'daki yeni konjonkturde ekonomik siyasi askeri ve kulturel bakimdan gucu anlatilmaya calisilmistir. Kuresellesmenin Avrupa Birligi uzerindeki etkileri anlatilmistir.
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    Conference Object
    A Reinforcement Learning Based Approach to Solve Voltage Issues in Distribution Networks
    (IEEE, 2025) Cakir, Muhammed Turhan; Nayir, Hasan; Demir, Alper; Kaya, Huseyin; Ceylan, Oguzhan
    This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve over-voltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.