Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - WoS: 4Citation - Scopus: 7Performance Analysis of Different Modulation Schemes for Underwater Acoustic Communications(Institute of Electrical and Electronics Engineers Inc., 2018-09) Bahcebasi, Akif; Güngör, Vehbi Çağrı; Tuna, GürkanThere is an increasing interest in using Underwater Acoustic Sensor Networks (UASNs) for various oceanographic applications, such as pollution monitoring, seismic monitoring, environmental data collection, offshore exploration, and tactical surveillance. UASNs rely on acoustic communications; however, the underwater acoustic channel is highly variable and its link quality depends on environmental factors and the locations of the communicating nodes. Therefore, ensuring reliable communication in UASNs is quite difficult. Moreover, path losses and retransmissions lead to the wastage of energy resources and reduce the network lifetime. In this study, we have utilized well-known underwater modulation schemes to analyse and simulate various underwater scenarios with different depth, distance and Bit Error Rate (BER) values in order to make a fair comparison between the modulation schemes and obtain the optimal transmission power. Performance evaluations show that 32-PSK and 16-QAM techniques achieve the minimum energy consumption rates and enhance network lifetime. © 2019 Elsevier B.V., All rights reserved.Book Part Hosting Capacity Calculation Methods(Elsevier, 2025) Oguzhan, Ceylan; Alper, SavasciIn this chapter, we focus on hosting capacity (HC) calculations, by giving the methods to determine the maximum amount of distributed energy resources (DER) that can be integrated into power distribution network(s) without compromising reliability or performance. We detail methodologies such as power flow-based approaches, probabilistic techniques, and machine learning algorithms, with sample applications of HC calculations. Initially, we focus on power flow-based methods based on simulating power distribution network(s) to assess system voltage, current flow, and stability impacts from DER installations. Then, we will give the probabilistic approaches that use uncertainties in renewable generation and consumer demand, based on statistical techniques and Monte Carlo simulations aiming to reflect these variability. Machine learning (ML) techniques will also be given based on analyzing large data sets, detecting patterns, and predicting system responses. These kinds of methods include regression analysis and neural networks trained on historical data for optimized HC predictions. It should be stated that HC is impacted by several factors, such as network topology, load profiles, and DER characteristics, and these as well will be discussed. We will provide a practical example of an HC calculation on a 141-node distribution network using a step-by-step algorithm in Matpower, with simulation results based on an iterative deterministic method. Then, we will give the broader implications of HC assessments for grid modernization and energy policy, highlighting how accurate calculations support a more decentralized, sustainable, and resilient energy future. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 12Ambient Energy Harvesting for Low Powered Wireless Sensor Network Based Smart Grid Applications(Institute of Electrical and Electronics Engineers Inc., 2019-04) Faheem, Muhammed Yasir; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Raza, Basit; Ngadi, M. A.; Güngör, Vehbi ÇağrıLimited battery lifetime is one of the most critical issues for wireless sensor networks (WSNs)-based smart grid (SG) applications. Recently, ambient energy harvesting (AEH) has been considered to significantly improve the network lifetime of the WSNs-based SG applications. However, extracting a significant amount of energy from the ambient energy resource due to time varying links quality affected by power grid environments is the main issue for WSNs-based applications in SG. In this paper, we propose a novel multi-source energy harvesting mechanisms for WSNs-based SG applications. The propose hybrid ambient energy harvesting framework through the designed circuitry successfully harvests massive power density by capturing the radial electric field (EF) and ambient radio frequency WiFi 2.4GHz band signals present in the vicinity of 500kV power grid station. The design energy harvesting schemes have been implemented on the recently developed routing protocol for SG applications. The experiments using EstiNet9.0, demonstrate that the designed framework is efficient in terms of energy harvesting capabilities to enable a long-lasting lifetime of the WSNs-based smart grid applications. © 2020 Elsevier B.V., All rights reserved.