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    A Probabilistic Approach to Modelling Home Appliances for Demand Side Management
    (Institute of Electrical and Electronics Engineers Inc., 2023) Çakil, Fatih; Tekdemir, Ibrahim Gürsu
    Demand-Side Management is a set of various optimization techniques and/or strategies based on techno-economic factors which focuses on planning, implementation and monitoring in power systems. In this context, energy consumption behavior of residential users is focused on and electrical appliances are modelled in various studies in literature. Electrical energy usage patterns of residential consumers and consumption behaviors are intended to be modelled in this study. We conducted a survey for modelling electrical appliances in residential houses, findings of which form the basis for developing probabilistic models designed for demand-side management applications. After that, we applied Monte Carlo sampling method to make the statistical data and relevant probabilistic models enable a thorough probabilistic simulation. 300 virtual consumers are created by using this approach as part of the simulation and relevant outcomes are obtained finally. Besides that, a graphical user interface (GUI) is created in MATLAB to demonstrate results. It is concluded that results of the simulation carried out in this study are useful in demand side management context and they may be used for studying new dynamic price models or for testing some DSM functions in future. © 2023 IEEE.
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    An Alternative Approach for Periodicity Detection in Various Time Series Data of Electrical Power Systems
    (Institute of Electrical and Electronics Engineers Inc., 2025) Tekdemir, Ibrahim Gürsu
    Proper analysis of time series data is an important task that should be considered in numerous engineering applications. Examining the periodic nature of such data has a greater impact in some fields such as dynamic analysis of power systems or analyzing the electrical energy consumption behavior of residential users. In this study, three types of time series data are handled, which are transient electrical current signal in a simulated power system, annual energy consumption data of real residential users, and synthetically created electrical power signal containing harmonic distortion with various high-frequency components. In this study, an alternative approach for the identification of time series characteristics is also proposed, which is based on statistical analysis with a different structure of the sampling window, and relevant results of periodicity detection analysis carried out for time series data are revealed. In addition to that, autocorrelation function of the same data is also calculated for comparison purposes. In conclusion, it is demonstrated that the proposed approach has significant properties in the context of periodicity detection when compared to the well-known autocorrelation function. It is a promising result for the proposed approach when considering performance in periodicity detection analysis realized for time series data. © 2025 IEEE.
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    Increasing Average Output Power by using a Fuzzy Logic Based Maximum Power Point Tracking Method in Wind Power Plants
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kursun, Nihat; Tekdemir, Ibrahim Gürsu
    In solar and wind power plants, it is desired that the power generated is always maximum. However, changes in the input parameters of the system may prevent this. Maximum power point tracking (MPPT) methods can be used especially in solar power plants. In wind power plants, speed and direction of the wind are always variable. As in solar power plants, instantaneous/average power amounts can be increased in wind power plants by using MPPT methods. In this study, Incremental Conductance, Perturb and Observe, Fuzzy Logic and models created without using any MPPT method were used. The average power values obtained by performing MATLAB/Simulink simulations were compared for the relevant scenarios. According to the results, it is seen that the output power can be increased by using MPPT methods. The highest increase was obtained by using Fuzzy Logic-based method. This is a beneficial result in terms of grid operation and energy planning. © 2024 IEEE.
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    Performance Analysis of Deterministic and Probabilistic Path Planning Algorithms in Complex Environments
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aksoy, Necati; Çakil, Fatih; Tekdemir, Ibrahim Gürsu
    Path planning, in an other saying navigation, algorithms are vital in assorted applications, including robotics, autonomous vehicles, and drones. These algorithms can be broadly categorized into deterministic and probabilistic methods along with other branches. This study focuses and examines two classical deterministic path planning algorithms, A-star (A*) and Dijkstra's algorithm, alongside two prominent probabilistic path planning algorithms, Rapidly-exploring Random Trees Star (RRT*) and Probabilistic Roadmap (PRM). In the paper, with creating multi-level building interior floor maps and testing the performance of these four algorithms are performed on each level. Performance metrics included execution time, CPU usage, memory usage, and path distance. The results, presented in comparative tables, provide a comprehensive analysis of the efficiency and resource demands of each algorithm. Furthermore, this research offers valuable insights for selecting appropriate path planning algorithms in various autonomous navigation applications, guiding future implementations in robotics, autonomous electric vehicles, and drone technology. © 2024 IEEE.
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    Real-Time Smart Power Distribution in Electric Vehicle Chargers: Utilizing Rust Programming for Enhanced Efficiency
    (Institute of Electrical and Electronics Engineers Inc., 2024) Çakil, Fatih; Aksoy, Necati; Tekdemir, Ibrahim Gürsu
    The increase in the number of electric vehicles (EVs) and charging units generates a substantial power demand, necessitating the effective management of these loads. This study examines a model that employs the Priority-Based Power Distribution approach to prioritize EVs at charging stations based on real-time power demand. By leveraging the advantages of the Rust programming language, we developed and simulated a real-time model framework. This research introduces an innovative interface and design for controller systems, illustrating the critical role of Rust programming in optimizing EV charging station operations. The results underscore the efficiency and practicality of our proposed model in effectively managing dynamic power distribution. © 2024 IEEE.