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Dr. Hegazy Rezk
Electrical Engineering, College of Engineering - Wadi Aldwaser, Prince Sattam bin Abdulaziz University, Wadi Aldwaser, Saudi Arabia

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Research Keywords & Expertise

0 Energy Efficiency
0 Energy Management
0 Renewable and Sustainable Energy
0 Modern optimization
0 Modeling based on artificial intelligence

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Energy Management
Energy Efficiency

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Review
Published: 31 July 2021 in Energies
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Magnetic refrigeration is a fascinating superior choice technology as compared with traditional refrigeration that relies on a unique property of particular materials, known as the magnetocaloric effect (MCE). This paper provides a thorough understanding of different magnetic refrigeration technologies using a variety of models to evaluate the coefficient of performance (COP) and specific cooling capacity outputs. Accordingly, magnetic refrigeration models are divided into four categories: rotating, reciprocating, C-shaped magnetic refrigeration, and active magnetic regenerator. The working principles of these models were described, and their outputs were extracted and compared. Furthermore, the influence of the magnetocaloric effect, the magnetization area, and the thermodynamic processes and cycles on the efficiency of magnetic refrigeration was investigated and discussed to achieve a maximum cooling capacity. The classes of magnetocaloric magnetic materials were summarized from previous studies and their potential magnetic characteristics are emphasized. The essential characteristics of magnetic refrigeration systems are highlighted to determine the significant advantages, difficulties, drawbacks, and feasibility analyses of these systems. Moreover, a cost analysis was provided in order to judge the feasibility of these systems for commercial use.

ACS Style

Ali Alahmer; Malik Al-Amayreh; Ahmad Mostafa; Mohammad Al-Dabbas; Hegazy Rezk. Magnetic Refrigeration Design Technologies: State of the Art and General Perspectives. Energies 2021, 14, 4662 .

AMA Style

Ali Alahmer, Malik Al-Amayreh, Ahmad Mostafa, Mohammad Al-Dabbas, Hegazy Rezk. Magnetic Refrigeration Design Technologies: State of the Art and General Perspectives. Energies. 2021; 14 (15):4662.

Chicago/Turabian Style

Ali Alahmer; Malik Al-Amayreh; Ahmad Mostafa; Mohammad Al-Dabbas; Hegazy Rezk. 2021. "Magnetic Refrigeration Design Technologies: State of the Art and General Perspectives." Energies 14, no. 15: 4662.

Journal article
Published: 16 July 2021 in Mathematics
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The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

ACS Style

Hesham Alhumade; Hegazy Rezk; Abdulrahim Al-Zahrani; Sharif Zaman; Ahmed Askalany. Artificial Intelligence Based Modelling of Adsorption Water Desalination System. Mathematics 2021, 9, 1674 .

AMA Style

Hesham Alhumade, Hegazy Rezk, Abdulrahim Al-Zahrani, Sharif Zaman, Ahmed Askalany. Artificial Intelligence Based Modelling of Adsorption Water Desalination System. Mathematics. 2021; 9 (14):1674.

Chicago/Turabian Style

Hesham Alhumade; Hegazy Rezk; Abdulrahim Al-Zahrani; Sharif Zaman; Ahmed Askalany. 2021. "Artificial Intelligence Based Modelling of Adsorption Water Desalination System." Mathematics 9, no. 14: 1674.

Journal article
Published: 23 June 2021 in Energy
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In this paper, an improved design approach for fuzzy logic control (FLC) systems is proposed for MPPT control of PEMFCs. The proposed design is based on the recent Equilibrium Optimizer method for determining the optimum parameters to fully benefit the inherent flexibility and freedom of FLC systems in addition to achieve fast and accurate tracking. During the optimization process, gains of membership functions of FLC is used as a decision variable, whereas the integral of error is used as a cost function. The obtained results are compared with particle swarm optimization (PSO), genetic algorithm (GA), electric charged particles optimization (ECPO) and spotted hyena optimizer (SHO). The proposed EO methodology outperformed all other methods, achieving the best mean, median, variance, and standard deviation. Moreover, statistical tests including Wilcoxon, Holm-Bonferroni correction, Kruskal Wallis, and Friedman tests are performed to prove efficiency of the proposed strategy. In last, different scenarios of changing operating temperature and water content are used to prove the reliability of the optimized FLC. The obtained results are compared with conventional FLC and hill-climbing method. The main findings confirm that the proposed design using combined features of EO and FLC presents a promising solution for MPPT in PEMFCs.

ACS Style

Hegazy Rezk; Mokhtar Aly; Ahmed Fathy. A Novel Strategy Based on Recent Equilibrium Optimizer to Enhance the Performance of PEM Fuel Cell System through Optimized Fuzzy Logic MPPT. Energy 2021, 234, 121267 .

AMA Style

Hegazy Rezk, Mokhtar Aly, Ahmed Fathy. A Novel Strategy Based on Recent Equilibrium Optimizer to Enhance the Performance of PEM Fuel Cell System through Optimized Fuzzy Logic MPPT. Energy. 2021; 234 ():121267.

Chicago/Turabian Style

Hegazy Rezk; Mokhtar Aly; Ahmed Fathy. 2021. "A Novel Strategy Based on Recent Equilibrium Optimizer to Enhance the Performance of PEM Fuel Cell System through Optimized Fuzzy Logic MPPT." Energy 234, no. : 121267.

Research article
Published: 15 June 2021 in International Journal of Energy Research
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A robust type-2 fuzzy logic (FL)-based approach for maximum power point tracking (MPPT) is proposed in this work. The method is used to increase the energy efficiency of thermoelectric generators (TEGs). Type-2 FL was applied to adapt the variable step size of incremental resistance (INR) MPPT to track the maximum available power of a TEG. The output power from a TEG relies mostly on the difference in temperature of its two sides added to the load value. Consequently, MPPT must be robust to extract the optimum operation point continually under varying operational conditions. The aim of the suggested approach is to enhance the dynamic response and eradicate fluctuations around the maximum power point (MPP). The results employing the type-2 FL are compared with conventional methods including INR, perturb and observe (P&O), and type-1 FL. With a variable load (15, 20, and 25 Ω), the proposed approach takes around 7 ms to reach a steady state with 2.6, 3.9, and 5.2 W overshoot, respectively, and almost zero oscillation. With a fixed load and a fixed temperature difference, our proposed tracker decreases the response time by 35.84%, 45.27%, and 96.50% compared to INR, P&O, and conventional FL, respectively. With a fixed load and a varying temperature difference, the proposed tracker decreases the response time by 53.33%, 94.07%, and 96.53% compared to INR, P&O, and conventional FL, respectively. The results confirmed the ability of the proposed method to keep the conversion efficiency of TEGs high and stable, reducing energy loss.

ACS Style

Hegazy Rezk; Abdelghani Harrag. A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems. International Journal of Energy Research 2021, 1 .

AMA Style

Hegazy Rezk, Abdelghani Harrag. A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems. International Journal of Energy Research. 2021; ():1.

Chicago/Turabian Style

Hegazy Rezk; Abdelghani Harrag. 2021. "A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems." International Journal of Energy Research , no. : 1.

Journal article
Published: 11 June 2021 in Expert Systems with Applications
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Under partial shading condition, the power-voltage curve of the photovoltaic (PV) system contains several maximum power points (MPPs). Among these points, there is only single global and some local points. Accordingly, modern optimization algorithms are highly required to tackle this problem. However, the methods are considered as time consuming. Therefore, finding a new algorithm that capable to solve the problem of tracking global maximum power point (GMPP) with minimum number of population is highly appreciated. Several new straightforward methods as well as meta-heuristic approaches are exist. Recently, the Marine Predator Algorithm (MPA) has been developed for engineering applications. In this study, an alternative method of MPA, integrating Opposition Based Learning (OBL) strategy with Grey Wolf Optimizer (GWO), named MPAOBL-GWO, is proposed to cope with the implied weaknesses of classical MPA. Firstly, Opposition Based Learning (OBL) strategy is adopted to prevent MPA method from searching deflation and to obtain faster convergence rate. Besides, the GWO is also implemented to further improve the swarm agents’ local search efficiency. Due to that, the MPA explores the search space well better than exploiting it; so, this combination improves the efficiency of the MPA and avoids it from falling in local points. To verify the effectiveness of the enhanced method, the well-known CEC’17 test suite and the maximum power point tracking (MPPT) of photovoltaic (PV) system problem are solved. The obtained results illustrate the ability of the proposed MPAOBL-GWO based method to achieve the optimum solution compared with the original MPA, GWO and Particle Swarm Optimization (PSO). The findings revealed that, the proposed method can be viewed as an efficient and effective strategy for more complex optimization scenarios and the MPPT as well.

ACS Style

Essam H. Houssein; Mohamed A. Mahdy; Ahmed Fathy; Hegazy Rezk. A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Systems with Applications 2021, 183, 115253 .

AMA Style

Essam H. Houssein, Mohamed A. Mahdy, Ahmed Fathy, Hegazy Rezk. A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Systems with Applications. 2021; 183 ():115253.

Chicago/Turabian Style

Essam H. Houssein; Mohamed A. Mahdy; Ahmed Fathy; Hegazy Rezk. 2021. "A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system." Expert Systems with Applications 183, no. : 115253.

Research article
Published: 05 June 2021 in International Journal of Energy Research
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This paper presents an optimal parameter identification strategy of the lithium-ion (Li-ion) battery model applying a recent metaheuristic artificial ecosystem-based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root-mean-square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show.

ACS Style

Seydali Ferahtia; Ali Djeroui; Hegazy Rezk; Aissa Chouder; Azeddine Houari; Mohamed Machmoum. Optimal parameter identification strategy applied to lithium‐ion battery model. International Journal of Energy Research 2021, 45, 16741 -16753.

AMA Style

Seydali Ferahtia, Ali Djeroui, Hegazy Rezk, Aissa Chouder, Azeddine Houari, Mohamed Machmoum. Optimal parameter identification strategy applied to lithium‐ion battery model. International Journal of Energy Research. 2021; 45 (11):16741-16753.

Chicago/Turabian Style

Seydali Ferahtia; Ali Djeroui; Hegazy Rezk; Aissa Chouder; Azeddine Houari; Mohamed Machmoum. 2021. "Optimal parameter identification strategy applied to lithium‐ion battery model." International Journal of Energy Research 45, no. 11: 16741-16753.

Journal article
Published: 28 May 2021 in Engineering Applications of Artificial Intelligence
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Meta-heuristic optimization algorithms aim to tackle real world problems through maximizing some specific criteria such as performance, profit, and quality or minimizing others such as cost, time, and error. Accordingly, this paper introduces an improved version of a well-known optimization algorithm namely Archimedes optimization algorithm (AOA). The enhanced version combines two efficient strategies namely Local escaping operator (LEO) and Orthogonal learning (OL) to introduce the (I-AOA) optimization algorithm. Moreover, the performance of the proposed I-AOA has been evaluated on the CEC’2020 test suite, and three engineering design problems. Furthermore, I-AOA is applied to determine the optimal parameters of polymer electrolyte membrane (PEM) fuel cell (FC). Two commercial types of PEM fuel cells: 250W PEMFC and BCS 500W are considered to prove the superiority of the proposed optimizer. During the optimization procedure, the seven unknown parameters (ξ1, ξ2, ξ3, ξ4, λ, RC, and b) of PEM fuel cell are assigned to be the decision variables. Whereas the cost function that required to be in a minimum state is represented by the RMSE between the estimated cell voltage and the measured data. The obtained results by the I-AOA are compared to other well-known optimizers such as Whale Optimization Algorithm (WOA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization Algorithm (PSO), Harris hawks optimization (HHO), Tunicate Swarm Algorithm (TSA) and original AOA. The comparison confirmed the superiority of the suggested algorithm in identifying the optimum PEM fuel cell parameters considering various operating conditions compared to the other optimization algorithms.

ACS Style

Essam H. Houssein; Bahaa El-Din Helmy; Hegazy Rezk; Ahmed M. Nassef. An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification. Engineering Applications of Artificial Intelligence 2021, 103, 104309 .

AMA Style

Essam H. Houssein, Bahaa El-Din Helmy, Hegazy Rezk, Ahmed M. Nassef. An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification. Engineering Applications of Artificial Intelligence. 2021; 103 ():104309.

Chicago/Turabian Style

Essam H. Houssein; Bahaa El-Din Helmy; Hegazy Rezk; Ahmed M. Nassef. 2021. "An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification." Engineering Applications of Artificial Intelligence 103, no. : 104309.

Journal article
Published: 13 May 2021 in Mathematics
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The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

ACS Style

Mohamed Afifi; Hegazy Rezk; Mohamed Ibrahim; Mohamed El-Nemr. Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya). Mathematics 2021, 9, 1107 .

AMA Style

Mohamed Afifi, Hegazy Rezk, Mohamed Ibrahim, Mohamed El-Nemr. Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya). Mathematics. 2021; 9 (10):1107.

Chicago/Turabian Style

Mohamed Afifi; Hegazy Rezk; Mohamed Ibrahim; Mohamed El-Nemr. 2021. "Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)." Mathematics 9, no. 10: 1107.

Journal article
Published: 10 May 2021 in Mathematics
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An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10−6 at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC.

ACS Style

Hesham Alhumade; Ahmed Fathy; Abdulrahim Al-Zahrani; Muhyaddin Rawa; Hegazy Rezk. Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization. Mathematics 2021, 9, 1066 .

AMA Style

Hesham Alhumade, Ahmed Fathy, Abdulrahim Al-Zahrani, Muhyaddin Rawa, Hegazy Rezk. Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization. Mathematics. 2021; 9 (9):1066.

Chicago/Turabian Style

Hesham Alhumade; Ahmed Fathy; Abdulrahim Al-Zahrani; Muhyaddin Rawa; Hegazy Rezk. 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization." Mathematics 9, no. 9: 1066.

Journal article
Published: 25 April 2021 in Mathematics
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This paper presents an enhancement method to improve the performance of the DC-link voltage loop regulation in a Doubly-Fed Induction Generator (DFIG)- based wind energy converter. An intelligent, combined control approach based on a metaheuristics-tuned Second-Order Sliding Mode (SOSM) controller and an adaptive fuzzy-scheduled Extended State Observer (ESO) is proposed and successfully applied. The proposed fuzzy gains-scheduling mechanism is performed to adaptively tune and update the bandwidth of the ESO while disturbances occur. Besides common time-domain performance indexes, bounded limitations on the effective parameters of the designed Super Twisting (STA)-based SOSM controllers are set thanks to the Lyapunov theory and used as nonlinear constraints for the formulated hard optimization control problem. A set of advanced metaheuristics, such as Thermal Exchange Optimization (TEO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search Algorithm (HSA), Water Cycle Algorithm (WCA), and Grasshopper Optimization Algorithm (GOA), is considered to solve the constrained optimization problem. Demonstrative simulation results are carried out to show the superiority and effectiveness of the proposed control scheme in terms of grid disturbances rejection, closed-loop tracking performance, and robustness against the chattering phenomenon. Several comparisons to our related works, i.e., approaches based on TEO-tuned PI controller, TEO-tuned STA-SOSM controller, and STA-SOSM controller-based linear observer, are presented and discussed.

ACS Style

Mohammed Alhato; Mohamed Ibrahim; Hegazy Rezk; Soufiene Bouallègue. An Enhanced DC-Link Voltage Response for Wind-Driven Doubly Fed Induction Generator Using Adaptive Fuzzy Extended State Observer and Sliding Mode Control. Mathematics 2021, 9, 963 .

AMA Style

Mohammed Alhato, Mohamed Ibrahim, Hegazy Rezk, Soufiene Bouallègue. An Enhanced DC-Link Voltage Response for Wind-Driven Doubly Fed Induction Generator Using Adaptive Fuzzy Extended State Observer and Sliding Mode Control. Mathematics. 2021; 9 (9):963.

Chicago/Turabian Style

Mohammed Alhato; Mohamed Ibrahim; Hegazy Rezk; Soufiene Bouallègue. 2021. "An Enhanced DC-Link Voltage Response for Wind-Driven Doubly Fed Induction Generator Using Adaptive Fuzzy Extended State Observer and Sliding Mode Control." Mathematics 9, no. 9: 963.

Journal article
Published: 09 April 2021 in Sustainability
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This paper identifies the best energy management strategy of hybrid photovoltaic–diesel battery-based water desalination systems in isolated regions using technical, economic and techno–economic criteria. The employed procedures include Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as tools for the solution. Twelve alternatives, containing three–four energy management strategies; four energy management strategies, load following (LF), cycle charging (CC), combined LF–CC, and predictive strategy; and three different sizes of brackish water reverse osmosis (BWRO) water desalination units, BWRO-150, BWRO-250, and BWRO-500, are investigated with capacity of 150, 250, and 500 m3/day, respectively. Eight attributes comprising different technical and economic metrics are considered during the evaluation procedure. HOMER Pro® software is utilized to perform the simulation and optimization. The main findings confirmed that the best energy management strategies are predictive strategies and the reverse osmosis (RO) unit’s optimal size is RO-250. For such an option, the annual operating cost and initial costs are $4590 and $78,435, respectively, whereas the cost of energy is $0.156/kWh. The excess energy and unmet loads are 27,532 kWh and 20.3 kWh, respectively. The breakeven grid extension distance and the amount of CO2 are 6.02 km and 14,289 kg per year, respectively. Compared with CC–RO-150, the amount of CO2 has been sharply decreased by 61.2%.

ACS Style

Hegazy Rezk; Basem Alamri; Mokhtar Aly; Ahmed Fathy; Abdul Olabi; Mohammad Abdelkareem; Hamdy Ziedan. Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System. Sustainability 2021, 13, 4202 .

AMA Style

Hegazy Rezk, Basem Alamri, Mokhtar Aly, Ahmed Fathy, Abdul Olabi, Mohammad Abdelkareem, Hamdy Ziedan. Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System. Sustainability. 2021; 13 (8):4202.

Chicago/Turabian Style

Hegazy Rezk; Basem Alamri; Mokhtar Aly; Ahmed Fathy; Abdul Olabi; Mohammad Abdelkareem; Hamdy Ziedan. 2021. "Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System." Sustainability 13, no. 8: 4202.

Research article
Published: 06 April 2021 in International Journal of Energy Research
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The output power from thermoelectric generator (TEG) system is mainly dependent on the differential temperature between the hot side and cold side of TEG in addition to the load demand. Therefore, maximum power point tracking (MPPT) control is highly needed to continuously track the optimal operating point with changing the operating condition. In this research paper, a MPPT method based on optimized fuzzy logic control (FLC) is proposed. The proposed method utilizes the freedom and flexibilities from FLC systems to develop accurate and fast‐tracking controller of maximum power point for TEG applications. The parameters of the optimized FLC have been identified using recent manta ray foraging optimization (MRFO) algorithm. During the proposed optimization process, the gains of the membership functions are used as decision variables, whereas the integral of the error is used as a cost function. Different scenarios of changing the difference temperature are used to prove the reliability of the optimized FLC. The obtained results by using the optimized FLC are compared with conventional FLC and the hill‐climbing methods. The main findings confirm that the proposed design using combined features of MRFO and FLC presents a promising solution for MPPT in TEG systems. The proposed optimized FLC method achieves superior performance through minimizing the fluctuations in the output power at the various studied scenarios. Moreover, continuous tracking of the maximum power from TEG is achieved by the proposed optimized FLC method at different hot side and cold side temperatures in addition to output load variations.

ACS Style

Mokhtar Aly; Hegazy Rezk. A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo‐electric generation systems. International Journal of Energy Research 2021, 45, 13897 -13910.

AMA Style

Mokhtar Aly, Hegazy Rezk. A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo‐electric generation systems. International Journal of Energy Research. 2021; 45 (9):13897-13910.

Chicago/Turabian Style

Mokhtar Aly; Hegazy Rezk. 2021. "A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo‐electric generation systems." International Journal of Energy Research 45, no. 9: 13897-13910.

Journal article
Published: 23 March 2021 in Measurement
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This paper is intended to determine the space-charge-free field on the stressed discharge wires’ surface, the corona-onset voltage of wire-duct electrostatic precipitators (ESP) as influenced by the variation of the velocities of the incoming flow gases. The corona current-voltage (I-V) characteristics of wire-duct ESP is calculated under varying velocities of incoming flow gases. The calculation is made using the improvement of Deutsch’s Method. The method is endorsed by an iterative process to determine an estimate for the underlying dissemination of the charge density close to the surface of the stressed discharge wire(s). The electric potential, field, space-charge density in the interelectrode spacing, corona onset voltage and current-voltage characteristics of the precipitator are considered. Besides, the effect of gradually increase of the velocities of incoming flow gases, changing the number of stressed wires and changing the wire radius of wire-duct ESP are investigated. An experimental set-up has made in the Laboratory of High Voltage Engineering, Czech Technical University (CTU) in Prague, Czech Republic to investigate the accuracy of mathematical/simulation analyzed of the corona-onset voltage as well as the validation of the developed Deutsch’s Method in modeling the I-V characteristics of wire-duct ESPs. The experimental results reasonably agree with the theoretical analysis.

ACS Style

Hamdy A. Ziedan; Hegazy Rezk; Mujahed Al-Dhaifallah; Ahmed Elnozahy. An experimental implementation and testing of the corona discharge in wire-duct electrostatic precipitators affected by velocities of incoming flow gases. Measurement 2021, 177, 109296 .

AMA Style

Hamdy A. Ziedan, Hegazy Rezk, Mujahed Al-Dhaifallah, Ahmed Elnozahy. An experimental implementation and testing of the corona discharge in wire-duct electrostatic precipitators affected by velocities of incoming flow gases. Measurement. 2021; 177 ():109296.

Chicago/Turabian Style

Hamdy A. Ziedan; Hegazy Rezk; Mujahed Al-Dhaifallah; Ahmed Elnozahy. 2021. "An experimental implementation and testing of the corona discharge in wire-duct electrostatic precipitators affected by velocities of incoming flow gases." Measurement 177, no. : 109296.

Journal article
Published: 22 March 2021 in Sustainability
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The dynamic voltage restorer (DVR) combined with a photovoltaic–thermoelectric generator (PV-TEG) system is proposed for voltage disturbance compensation in the three-phase four-wire distribution system. The PV-TEG hybrid energy source is used in the DVR system to improve the system ability for deep and long-period power quality disturbance compensation. In addition, the DVR will save grid energy consumption when the hybrid PV-TEG module generates sufficient power to meet the load demand. An enhanced variable factor adaptive fuzzy logic controller (VFAFLC)-based maximum power point tracking (MPPT) control scheme is proposed to extract the maximum possible power from the PV module. Since the PV and TEG combine a hybrid energy source for generating power, the DVR can work efficiently for the voltage sag/swell, outage compensation, and energy conservation mode with minimum energy storage facilities. The in-phase compensation method and the three-leg voltage source inverter (VSI) circuit are chosen in the present system for better voltage and/or power compensation. To confirm the effectiveness of the proposed hybrid PV-TEG integrated DVR system, a simulation-based investigation is carried out with four different operational modes with MATLAB software. The study results confirm that the proposed DVR system can compensate power quality disturbances of the three-phase load with less total harmonics distortion (THD) and will also work efficiently under energy conservation mode to save grid energy consumption. Moreover, the proposed VFAFLC-based control technique performs better to achieve the maximum power point (MPP) quickly and accurately, thereby improving the efficiency of the hybrid energy module.

ACS Style

N. Kanagaraj; Hegazy Rezk. Dynamic Voltage Restorer Integrated with Photovoltaic-Thermoelectric Generator for Voltage Disturbances Compensation and Energy Saving in Three-Phase System. Sustainability 2021, 13, 3511 .

AMA Style

N. Kanagaraj, Hegazy Rezk. Dynamic Voltage Restorer Integrated with Photovoltaic-Thermoelectric Generator for Voltage Disturbances Compensation and Energy Saving in Three-Phase System. Sustainability. 2021; 13 (6):3511.

Chicago/Turabian Style

N. Kanagaraj; Hegazy Rezk. 2021. "Dynamic Voltage Restorer Integrated with Photovoltaic-Thermoelectric Generator for Voltage Disturbances Compensation and Energy Saving in Three-Phase System." Sustainability 13, no. 6: 3511.

Journal article
Published: 17 March 2021 in Energies
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This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.

ACS Style

Seydali Ferahtia; Ali Djeroui; Tedjani Mesbahi; Azeddine Houari; Samir Zeghlache; Hegazy Rezk; Théophile Paul. Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System. Energies 2021, 14, 1660 .

AMA Style

Seydali Ferahtia, Ali Djeroui, Tedjani Mesbahi, Azeddine Houari, Samir Zeghlache, Hegazy Rezk, Théophile Paul. Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System. Energies. 2021; 14 (6):1660.

Chicago/Turabian Style

Seydali Ferahtia; Ali Djeroui; Tedjani Mesbahi; Azeddine Houari; Samir Zeghlache; Hegazy Rezk; Théophile Paul. 2021. "Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System." Energies 14, no. 6: 1660.

Journal article
Published: 08 March 2021 in Mathematics
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The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

ACS Style

Mohamed El-Nemr; Mohamed Afifi; Hegazy Rezk; Mohamed Ibrahim. Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II). Mathematics 2021, 9, 576 .

AMA Style

Mohamed El-Nemr, Mohamed Afifi, Hegazy Rezk, Mohamed Ibrahim. Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II). Mathematics. 2021; 9 (5):576.

Chicago/Turabian Style

Mohamed El-Nemr; Mohamed Afifi; Hegazy Rezk; Mohamed Ibrahim. 2021. "Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)." Mathematics 9, no. 5: 576.

Journal article
Published: 18 February 2021 in Mathematics
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This paper introduces a novel sensorless model-predictive torque-flux control (MPTFC) for two-level inverter-fed induction motor (IM) drives to overcome the high torque ripples issue, which is evidently presented in model-predictive torque control (MPTC). The suggested control approach will be based on a novel modification for the adaptive full-order-observer (AFOO). Moreover, the motor is modeled considering core losses and a compensation term of core loss applied to the suggested observer. In order to mitigate the machine losses, particularly at low speed and light load operations, the loss minimization criterion (LMC) is suggested. A comprehensive comparative analysis between the performance of IM drive under conventional MPTC, and those of the proposed MPTFC approaches (without and with consideration of the LMC) has been carried out to confirm the efficiency of the proposed MPTFC drive. Based on MATLAB® and Simulink® from MathWorks® (2018a, Natick, MA 01760-2098 USA) simulation results, the suggested sensorless system can operate at very low speeds and has the better dynamic and steady-state performance. Moreover, a comparison in detail of MPTC and the proposed MPTFC techniques regarding torque, current, and fluxes ripples is performed. The stability of the modified adaptive closed-loop observer for speed, flux and parameters estimation methodology is proven for a wide range of speeds via Lyapunov’s theorem.

ACS Style

Ahmed Aziz; Hegazy Rez; Ahmed Diab. Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives. Mathematics 2021, 9, 403 .

AMA Style

Ahmed Aziz, Hegazy Rez, Ahmed Diab. Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives. Mathematics. 2021; 9 (4):403.

Chicago/Turabian Style

Ahmed Aziz; Hegazy Rez; Ahmed Diab. 2021. "Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives." Mathematics 9, no. 4: 403.

Journal article
Published: 16 February 2021 in Electronics
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In this paper, a modified version of a recent optimization algorithm called gradient-based optimizer (GBO) is proposed with the aim of improving its performance. Both the original gradient-based optimizer and the modified version, MGBO, are utilized for estimating the parameters of Photovoltaic models. The MGBO has the advantages of accelerated convergence rate as well as avoiding the local optima. These features make it compatible for investigating its performance in one of the nonlinear optimization problems like Photovoltaic model parameters estimation. The MGBO is used for the identification of parameters of different Photovoltaic models; single-diode, double-diode, and PV module. To obtain a generic Photovoltaic model, it is required to fit the experimentally obtained data. During the optimization process, the unknown parameters of the PV model are used as a decision variable whereas the root means squared error between the measured and estimated data is used as a cost function. The results verified the fast conversion rate and precision of the MGBO over other recently reported algorithms in solving the studied optimization problem.

ACS Style

Mohamed Hassan; Salah Kamel; M. El-Dabah; Hegazy Rezk. A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models. Electronics 2021, 10, 472 .

AMA Style

Mohamed Hassan, Salah Kamel, M. El-Dabah, Hegazy Rezk. A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models. Electronics. 2021; 10 (4):472.

Chicago/Turabian Style

Mohamed Hassan; Salah Kamel; M. El-Dabah; Hegazy Rezk. 2021. "A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models." Electronics 10, no. 4: 472.

Journal article
Published: 10 February 2021 in IEEE Access
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Fuel cell (FC) represents one of the promising efficient solutions for future energy supply. Improving performance and integration methods of FCs via maximum power point tracking (MPPT) and high boosting factor inverters are key requirements for research in renewable energy fields. Recently, hybrid FC-battery structures have shown wide applications in several areas. Accordingly, marine predators algorithm (MPA) is proposed in this article for optimizing the design of reduced sensor fuzzy-logic based MPPT scheme. The proposed scheme inherits the following benefits: reduced sensors and hence reduced costs, more flexibility and smooth performance due to fuzzy-logic based MPPT, and optimized design method of fuzzy-logic based MPPT through MPA method. Moreover, a high boosting ratio inverter is introduced in this article based on using the switched capacitor multilevel inverter (SCMLI). The proposed system achieves self capacitor voltage control without complex control or extra sensors. The proposed hybrid FC-battery system has been validated at various operating points. In addition, comprehensive comparisons with existing schemes in the literature are provided in the paper. The superiority of the proposed scheme has been verified with robust, fast and accurate tracking, reduced cost, flexible, simple, and smooth output waveforms. The proposed method achieves the lowest output power fluctuations with fast tracking speed compared to the studied classical methods.

ACS Style

Mokhtar Aly; Emad M. Ahmed; Hegazy Rezk; Emad A. Mohamed. Marine Predators Algorithm Optimized Reduced Sensor Fuzzy-Logic Based Maximum Power Point Tracking of Fuel Cell-Battery Standalone Applications. IEEE Access 2021, 9, 27987 -28000.

AMA Style

Mokhtar Aly, Emad M. Ahmed, Hegazy Rezk, Emad A. Mohamed. Marine Predators Algorithm Optimized Reduced Sensor Fuzzy-Logic Based Maximum Power Point Tracking of Fuel Cell-Battery Standalone Applications. IEEE Access. 2021; 9 ():27987-28000.

Chicago/Turabian Style

Mokhtar Aly; Emad M. Ahmed; Hegazy Rezk; Emad A. Mohamed. 2021. "Marine Predators Algorithm Optimized Reduced Sensor Fuzzy-Logic Based Maximum Power Point Tracking of Fuel Cell-Battery Standalone Applications." IEEE Access 9, no. : 27987-28000.

Journal article
Published: 09 February 2021 in Mathematics
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The torque density and efficiency of synchronous reluctance machines (SynRMs) are greatly affected by the geometry of the rotor. Hence, an optimal design of the SynRM rotor geometry is highly recommended to achieve optimal performance (i.e., torque density, efficiency, and power factor). This paper studies the impact of considering the current angle as a variable during the optimization process on the resulting optimal geometry of the SynRM rotor. Various cases are analyzed and compared for different ranges of current angles during the optimization process. The analysis is carried out using finite element magnetic simulation. The obtained optimal geometry is prototyped for validation purposes. It is observed that when considering the effect of the current angle during the optimization process, the output power of the optimal geometry is about 3.32% higher than that of a fixed current angle case. In addition, during the optimization process, the case which considers the current angle as a variable has reached the optimal rotor geometry faster than that of a fixed current angle case. Moreover, it is observed that for a fixed current angle case, the torque ripple is affected by the selected value of the current angle. The torque ripple is greatly decreased by about 34.20% with a current angle of 45° compared to a current angle of 56.50°, which was introduced in previous literature.

ACS Style

Hegazy Rezk; Kotb B. Tawfiq; Peter Sergeant; Mohamed Ibrahim. Optimal Rotor Design of Synchronous Reluctance Machines Considering the Effect of Current Angle. Mathematics 2021, 9, 344 .

AMA Style

Hegazy Rezk, Kotb B. Tawfiq, Peter Sergeant, Mohamed Ibrahim. Optimal Rotor Design of Synchronous Reluctance Machines Considering the Effect of Current Angle. Mathematics. 2021; 9 (4):344.

Chicago/Turabian Style

Hegazy Rezk; Kotb B. Tawfiq; Peter Sergeant; Mohamed Ibrahim. 2021. "Optimal Rotor Design of Synchronous Reluctance Machines Considering the Effect of Current Angle." Mathematics 9, no. 4: 344.