Yazar "Ozturk, A." seçeneğine göre listele

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    The Effect of Economic Conditions on Optimal Sizing and Feasibility of Hybrid Power System
    (Institute of Electrical and Electronics Engineers Inc., 2023) Terkes, M.; Demirci, A.; Ozturk, Z.; Ozturk, A.; Tosun, S.; Andic, C.
    Clean energy-focused hybrid power systems (HPS) can help countries with their sustainable development plans. Optimal sizing of HPS depending on the varying countries' economic conditions in a reliable and robust strategy can reduce investment risks. This study performed optimal PV-DG-ESS based HPS sizing and feasibility analyses for Antalya, considering Türkiye's last ten years' interest and inflation rates, energy purchase costs, diesel costs, HPS investment, and operating costs. Moreover, the scope of the study is extended by sensitivity analyses on solar radiation, interest, and inflation. The results show that the proposed optimal HPS can reduce electricity purchase costs by up to $143.8 thousand/year and increase the renewable fraction by up to 74%, considering the current economic and climatic conditions. According to the sensitivity analysis, the grid was preferred in scenarios where solar radiation was lower than 5.5 kWh/m2/day. The research revealed the impact of economic conditions on HPS and is expected to guide investors and policymakers. © 2023 IEEE.
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    Enhancing State Estimation Accuracy in Power Systems: An ANN-Based Data Mining Approach Defending Cyber Attacks
    (Institute of Electrical and Electronics Engineers Inc., 2023) Andic, C.; Ozturk, A.; Turkay, B.
    In energy systems, measurement accuracy is jeopardized by bad data arising from cyber attacks. When bad data is detected in the measurement dataset as a result of cyber attacks, it's essential to identify and eliminate these data. However, this elimination process introduces the problem of missing measurement data, threatening the system's observability conditions. This study proposes a data mining approach supported by artificial neural networks to address the missing measurement data issue when bad data is detected. Our proposed method aims to maintain the system's observability by completing the measurement data lost due to bad data. Consequently, the measurement set purified from bad data enhances the accuracy of the crow search algorithm based state estimation results. This methodology has been shown to successfully mitigate the adverse effects of unforeseen situations, such as cyber attacks. © 2023 IEEE.
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    A novel sea horse optimizer based load frequency controller for two-area power system with PV and thermal units
    (Association for Scientific Computing Electronics and Engineering (ASCEE), 2024) Andic, C.; Ozumcan, S.; Varan, M.; Ozturk, A.
    This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods. © 2024, Association for Scientific Computing Electronics and Engineering (ASCEE). All rights reserved.