Yazar "Abdulqader, Qutada Jihad" seçeneğine göre listele

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    Öğe
    Enhancing smart grid stability with the implementation of heuristic algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Abdulqader, Qutada Jihad; Abdulazeez, Mshhain Ghazi; Hamodat, Zaid
    The smart grid, one of the most technologically advanced systems in existence today, is responsible for balancing supply and demand (DR). Residential customers have a significant influence on the overall operation of the conventional power system due to their high levels of energy consumption. HEM is a system designed to assist consumers in monitoring, regulating, and decreasing their energy use. With the use of HEM, appliances may be designed so that their consumption is changed to match the quantity of available supply. Recent advances in artificial intelligence have facilitated the attainment of these goals (AI). Heuristic approaches include optimization techniques such as wind-driven optimization (WDO), genomics optimization (GA), and binary particle swarm optimization (BPSO) (BPSO). Simulations are used to evaluate scheduling alternatives based on parameters such as cost, peak-to-average ratio (PAR), and an equally distributed power demand pattern throughout the system. Simulation results indicate that the WDO-based HEM outperforms both the BPSO and the GA algorithms.
  • Küçük Resim
    Öğe
    Solar energy control system based on metaheuristic method
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2023) Abdulqader, Qutada Jihad; Kurnaz, Sefer
    This thesis examines the issue of figuring out the particle swarm optimization method's maximum power point tracking algorithm for solar energy systems. Before putting a solar cell to use, the manufacturer typically characterizes the device using empirical data. Measurement or detection of a variety of solar energy degradation pathways is important. Thus, it is advantageous to actively measure solar energy factors throughout time. We provide an approach to enhance the maximum power point tracking algorithm for an equivalent model of a typical solar energy system using a smart method based on particle swarm optimization. This technique enables routine updating of the solar energy system, which can be used to identify the system's maximum output power. The 1400 Watt was reached for PV power efficiency, and the irradiation value was 1000 Watt/m2. 95.45% of both the suggested method and the MPPT method were accurate. Its precision is measured in terms of maximum power (252.44 Watts), cells per module (40), and open circuit voltage (35.44 Volts), accordingly.