WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 3Citation - Scopus: 3Investigation of Distributed Series Reactors in Power System Applications and Its Economic Implementation(Wiley, 2016-08-18) Onen, AhmetThe transmission system expansion planning process requires lots of calculations looking many years into the future, and the results are based on assumed load growth. If the load growth assumed in the planning process is not correct and unexpected load growth occurs for some load points, the transmission system could face serious congestion and even overloading problems. In this paper, transmission line impedance adjustment techniques using distributed series reactance (DSR) is considered. The DSRs can be used to control power flow and alleviate overloading problems. A new term, DSR congestion relief factor, is introduced. The DSR congestion relief factor measures the increase of transmission line capacity with the application of DSRs. Parametric studies run on the IEEE 39-bus system are presented. These studies investigate the economic benefits of DSRs and the use of DSRs for single contingencies and compare DSRs with existing technologies for expanding the transmission system.Article AI-Enhanced PV Power Forecasting Using Cloud Thickness and Motion in Kayseri, Türkiye(Wiley, 2025-01) Yavuz, Levent; Onen, Ahmet; Awad, Ahmed; Ahshan, Razzaqul; Al-Badi, AbdullahThe incorporation of renewable energy in photovoltaic (PV) systems has made significant progress. The inherent intermittency nature of PV generation, nevertheless poses an obstacle to accurate energy forecasting. Historical PV production plus meteorological data such as temperature, humidity, and atmospheric pressure are largely utilized in present methods of forecasting. However, cloud thickness and dynamics-integrated system, has not been investigated and tested in real-world examples yet.This research seeks to fill this gap in research through the development of a new AI-based PV forecasting model that incorporates cloud thickness, cloud motion, and solar position into the forecasting model. Cloud properties and their impact on solar radiation are computed through a deep learning-based panel-shadowing model. For cloud movement forecasting, a gated recurrent unit (GRU) is used, while multiple convolutional neural networks (CNNs) are used for estimating cloud thickness. These outcomes are then integrated with measurements from environmental sensors to improve the accuracy of the predictions.The system was implemented and tested at Abdullah G & uuml;l University and exhibited a remarkable improvement in forecasting accuracy compared to current models. The results prove that cloud motion and thickness improve the accuracy of PV predictions, which is important for energy market stability and power grid operations.