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Hamodat, Zaid
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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An Accurate Efficiency Calculation for PMSG Utilized in Renewable Energy Systems Hamodat, Zaid; Hussein, Ismail Khudhur; Nasir, Bilal Abdullah
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18441

Abstract

Considering the importance of optimizing renewable energy systems, this paper aims at calculating the exact efficiency of a stand-alone wind turbine connected to a synchronous generator with permanent magnet excitation (PMSG). By accounting for mechanical and electrical losses (copper losses, stray load losses, iron core losses, friction losses, windings losses, and magnetizing saturation effect), the study investigates the impact of wind speed on the generator's performance and efficiency in addition to the impact of losses on the overall efficiency of (PMSG). The simulation of the PMSG dynamic model 8.5×(10)^3 V․A is executed using MATLAB/Simulink, employing a simplified equivalent circuit that accurately represents the PMSG's behavior under steady-state conditions with resistive loads. Wind speeds of 12 and 14 meters/second are chosen as fixed values to demonstrate the effect of varying wind speed on efficiency. The obtained results reveal the influence of wind speed on the PMSG efficiency. The presented findings contribute to the understanding of PMSG performance and can aid in optimizing the stand-alone wind turbine systems, they also show that the wind had an effect on the efficiency values that were obtained (97.86% at 12m/s and 97.91% at 14 m/s), while the effect of losses was very few around 3%. However, the obtained results are very good compared to previous studies to show the accuracy and validity of the suggested dynamic model.
Design of Power Control Circuit for Grid-Connected PV System-Based Neural Network Al-Jaboury, Omar N. Rajab; Hamodat, Zaid; Daoud, Raid W.
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.20751

Abstract

This research explores the application of neural networks in managing grid- photovoltaic (PV) systems. this paper aims to improve the performance and reliability of PV systems using artificial intelligence capabilities, specifically neural networks. The main emphasis of this system is to control active and reactive power and to track the maximum power point (MPPT). This study introduces an intelligent control technique for fuel cell distributed generation (DG) grid connection inverters. The algorithm allows for the management of both active and reactive power for the unit. The algorithm provides local reactive power compensation, making it economically viable. The controller modeling and performance validation are conducted using MATLAB/Simulink and Sim power system blocks, demonstrating its capacity for enhancing power factor. This makes fuel cell technology a clean, highly controllable, and economically viable option for DG systems. The system maximizes the energy extraction of PV panels and maintains them at their ideal PowerPoint across various environmental conditions. It also raises the voltage from 260 volts to 350 volts.  Simulations and practical evaluations validate the proposed control system. The obtained results indicate that the total harmonic distortion (THD) of the grid current under operating conditions was less than 1.86%. This demonstrates significant improvements in the efficiency and reliability of PV systems. The neural network controller shows remarkable flexibility and the ability to quickly adapt to fluctuations in load and radiation, which contributes to developing a more sustainable and stable energy network.
Co-Authors Al-Jaboury, Omar N. Rajab Daoud, Raid W. Hussein, Ismail Khudhur Nasir, Bilal Abdullah