Browsing by Author "Kamel, Salah"

Now showing 1 - 3 of 3
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    Improved quadratic interpolation optimizer for stochastic short-term hydrothermal scheduling with integration of solar PV and wind power
    (Springer Nature, 2025-04-02) Khan, Noor Habib; Wang, Yong; Jamal, Raheela; Ebeed, Mohamed; Kamel, Salah; Ali, Guma; Jurado, Francisco; Youssef, Abdel-Raheem
    The quadratic interpolation optimization (QIO) introduces a novel approach inspired by the generalized quadratic interpolation (GQI) with dual mechanisms. Initially, QIO employs GQI in its exploration strategy, updating populations based on two randomly selected individuals. Subsequently, it incorporates another exploration strategy, updating populations based on the best solution and two randomly selected individuals. Despite QIO’s effectiveness in numerous optimization tasks, it exhibits limitations when addressing highly nonlinear and multidimensional problems, such as stagnation, susceptibility to local optima, low diversity, and premature convergence. In this study, we propose three enhancement strategies to refine traditional QIO, aiming to bolster its exploration and exploitation capabilities through Weibull flight motion, chaotic mutation, and PDO mechanisms. The resultant improved QIO (IQIO) is then applied to solve the short-term hydrothermal scheduling (STHS) problem, considering system uncertainties and the potential installation of PV and wind turbine generation units to reduce fuel costs and emissions. The STHS is solved with considering the system constraints including water discharge and reservoir storage, the generated powers by the hydro and thermal units as well as balanced powers. The dependent constraints are handled using weighted summation method. The efficacy of the proposed IQIO is demonstrated using the CEC 2022 test suite, and the obtained results are benchmarked against various competitive optimization methods. Statistical analysis of the results confirms a notable enhancement in the original QIO’s performance upon applying the suggested IQIO. Furthermore, the inclusion of renewable generation units by IQIO yields maximum reductions of 23.73% in costs and 45.50% in emissions, underscoring its potential for sustainable energy management.
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    Model predictive control based on single-phase shift modulation for triple active bridge DC-DC converter
    (Springer Nature, 2024-12-05) Adam, Ahmed Hamed Ahmed; Chen, Jiawei; Xu, Minghan; Kamel, Salah; Ali, Guma
    The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter. The developed MPC offers improved transient performance, control flexibility, and precision, ensuring compliance with DC voltage regulations and achieving optimal solutions for port decoupling. The MPC utilizes a cost function to provide robust voltage regulation, and an algorithm based on Karush-Kuhn-Tucker (KKT) conditions is developed to derive closed-form solutions for optimal control parameters. To validate the performance of the TAB converter with the proposed MPC control, Typhoon 602 hardware-in-loop (HIL) experimental case study is conducted. Additionally, a comparison with previous works is carried out to confirm the effectiveness of the proposed method. The results of the HIL experimental setup and the comparative analysis demonstrate that the developed method is effective, providing faster dynamic characteristics and port power decoupling operation capability.
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    Model predictive control for quad active bridge DC-DC converter for more electric aircraft applications
    (Springer Nature, 2025-04-21) Adam, Ahmed Hamed Ahmed; Chen, Jiawei; Xu, Minghan; Kamel, Salah; Ali, Guma
    The isolated multi-port converters quad-active bridge (QAB) presents a unique opportunity to connect multiple sources and loads operating at different power and voltage levels, offering galvanic isolation and shared magnetics as advantages. However, the high number of modulation variables, dynamic response, and overall modeling complexity of QAB converters pose challenges to controller design. Traditional linear controllers often struggle with voltage overshooting and undershooting under abrupt load changes and exhibit limited dynamic performance and coupling among different ports. To address these challenges, this paper introduces a moving discretized control set-model predictive control (MDCS-MPC) strategy for QAB converters. The developed approach predicts phase shift values through the converter model, ensuring fast dynamic performance and eliminating steady-state errors in control variables. The prediction model’s embedded circuit parameters and operating modes enhance performance across various power and terminal voltage ranges. An adaptive step is implemented for quick transitions, significantly reducing computational demands. These analytical findings and the MDCS-MPC strategy are verified through Matlab simulation results and experimental results obtained from the Hardware-in-the-Loop (HIL) real-time Typhoon 602 platform. Both experimental and simulation results demonstrate the effectiveness of the developed strategy, showing superior dynamic response, robustness, and reduced computational requirements. Furthermore, the voltage achieves a very fast dynamic response and exhibits no significant voltage overshoot or undershoot.