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Prof. Dr. Attia El-Fergany
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0 Artifical Intelligence
0 Soft Computing and Applications
0 Protection and Control
0 Renewable and non-renewable power plants
0 Power & energy

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Short Biography

Attia El-Fergany (SM’14) received the BSc degree (1994), MSc degree (1998) and PhD degree (2001), all in Electrical Power Engineering from Zagazig University in Zagazig, Egypt. He has been with the University of Zagazig since 1998, presently as Full Professor. El-Fergany has authored/co-authored numerous articles published in the refereed renowned journals. Attia has been given many awards for distinct international publishing. In addition, he delivered numerous short courses and participated in many field electrical technical studies. He is an Associate Editor of the AEJ and BEEI journals. He is a Senior Member of the IEEE, and a Member of the IET. His research is concerned with the use of intelligent techniques to solve electric power system problems.

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Journal article
Published: 23 August 2021 in Sustainability
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The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).

ACS Style

Ahmed Ginidi; Sherif M. Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Attia El-Fergany. Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems. Sustainability 2021, 13, 9459 .

AMA Style

Ahmed Ginidi, Sherif M. Ghoneim, Abdallah Elsayed, Ragab El-Sehiemy, Abdullah Shaheen, Attia El-Fergany. Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems. Sustainability. 2021; 13 (16):9459.

Chicago/Turabian Style

Ahmed Ginidi; Sherif M. Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Attia El-Fergany. 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems." Sustainability 13, no. 16: 9459.

Research article
Published: 20 August 2021 in International Journal of Energy Research
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Reliable and precise parameters' identification of the solid oxide fuel cell (SOFC) models is vital for the simulation and analysis of its steady-state and dynamic behaviors. However, the SOFC model is characterized by multimodal, high nonlinearity, and strong nonconvexity natures. Therefore, this paper develops a novel variant of the chameleon swarm algorithm (CSA), named memory-based chameleon swarm algorithm (MCSA), to extract highly accurate and precise parameters of SOFC model. In the MCSA, the search mechanism of the chameleon is guided using an internal memory to keep track of the best solutions in previous generations using neighborhood searching strategy, experience-based crossover scheme, and greedy selection strategy. These strategies can provide an appropriate balancing among the global exploration and local exploitation phases. Besides, the stored solutions in the memory can enhance the population diversity and accordingly increase the propensity of reaching the global optimal efficiently. The proposed MCSA is examined on various scenarios of the SOFC model under the steady-state and dynamic state manners. The simulation results of the proposed algorithm are confirmed and validated via comprehensive comparisons using the statistical measures and Friedman nonparametric test with other recent counterparts. The conducted comparisons and analyses have affirmed the efficacy and superiority of the proposed MCSA through achieving accurately identified parameters, where minimum deviation among the estimated and measured stack current-voltage and stack current-power curves is exhibited.

ACS Style

Rizk M. Rizk‐Allah; Mohamed A. El‐Hameed; Attia A. El‐Fergany. Model parameters extraction of solid oxide fuel cells based on semi‐empirical and memory‐based chameleon swarm algorithm. International Journal of Energy Research 2021, 1 .

AMA Style

Rizk M. Rizk‐Allah, Mohamed A. El‐Hameed, Attia A. El‐Fergany. Model parameters extraction of solid oxide fuel cells based on semi‐empirical and memory‐based chameleon swarm algorithm. International Journal of Energy Research. 2021; ():1.

Chicago/Turabian Style

Rizk M. Rizk‐Allah; Mohamed A. El‐Hameed; Attia A. El‐Fergany. 2021. "Model parameters extraction of solid oxide fuel cells based on semi‐empirical and memory‐based chameleon swarm algorithm." International Journal of Energy Research , no. : 1.

Journal article
Published: 27 July 2021 in Applied Soft Computing
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This manuscript announces a novel hybrid optimization model comprising the gradient-based optimizer (GBO) and Linear population size reduction technique of Success History-based Adaptive Differential Evolution (LSHADE) algorithm, so-called GB-LSHADE, for optimal relay coordination problem (ORCP). The ORCP is framed as a nonlinear constrained engineering problem that aims to optimize the time-dial, pickup current, and relay characteristics so that the relay clearance times are minimized. The proposed GB-LSHADE operates in two phases meanwhile the first phase starts with GBO to enrich the exploitative tendencies while the second phase is adopted to boost the local searching competence and avoid the premature convergence. To verify the efficacy of the proposed optimization method, a comprehensive simulation has been conducted on three test-systems (8-bus, 15-bus, and IEEE 30-bus) with different complexities. Many scenarios including fixed and varied relay curves as per IEC 60255 are demonstrated. At a final stage of this work, the computational complexity of the proposed approach is carried out and analyzed. It can be confirmed that the adapted optimal settings produced by the GB-LSHADE achieve the full co-ordination of the protection without any violations for the systems under study. In addition, the numerical simulated results show that the GB-LSHADE supersedes the other viable optimizers reported in specialized literature thru further validations and comprehensive comparisons.

ACS Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. Effective coordination settings for directional overcurrent relay using hybrid Gradient-based optimizer. Applied Soft Computing 2021, 107748 .

AMA Style

Rizk M. Rizk-Allah, Attia A. El-Fergany. Effective coordination settings for directional overcurrent relay using hybrid Gradient-based optimizer. Applied Soft Computing. 2021; ():107748.

Chicago/Turabian Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. 2021. "Effective coordination settings for directional overcurrent relay using hybrid Gradient-based optimizer." Applied Soft Computing , no. : 107748.

Journal article
Published: 26 July 2021 in Energy
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This paper presents an emended Heap-based optimizer (EHBO) for characterizing the accurate-performance of three-diode-based model (3DbM) of solar generating units (SGUs). The accurate extraction of the 3DbM parameters is an important issue to meet the actual characteristics of SGUs. Therefore, a better-quality version of HBO, named EHBO, is proposed to attain the same. As the complicated natures accompanied with the 3DbM such as multi-variable, multi-modal as well as its sensitivity to tiny changes, an EHBO is introduced through two improvements viz Hill-Climbing strategy (HCS), and informed searching-based learning strategy (ISLS). The HCS assists the algorithm to attain the promising areas of the search space and then enriches the diversity as well as the exploration capability while the ISLS aims to bolster the quality of the best individual and thus, can enable the exploitation ability. Therefore, EHBO is faster and more accurate than the classical HBO in achieving the global optimum as well as balancing exploration and exploitation abilities. The proposed EHBO is investigated on three commercial SGUs, namely, PWP-201, Kyocera polycrystalline KC200GT, and Ultra 85-P with maximum cropped errors of 2.0507 mA, 0.2211 mA, and 2.417 mA for these modules; respectively. Comprehensive experiments with comparisons are conducted to show the effectiveness and efficacy of the EHBO based model versus other competing optimizers. Based on the conducted investigations, it can be confirmed that the EHBO is a promising optimization method to deal with uncertain parameters of SGUs with different technologies.

ACS Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model. Energy 2021, 237, 121561 .

AMA Style

Rizk M. Rizk-Allah, Attia A. El-Fergany. Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model. Energy. 2021; 237 ():121561.

Chicago/Turabian Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. 2021. "Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model." Energy 237, no. : 121561.

Journal article
Published: 20 July 2021 in Sustainability
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A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.

ACS Style

Sherif Ghoneim; Mohamed Kotb; Hany Hasanien; Mosleh Alharthi; Attia El-Fergany. Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis. Sustainability 2021, 13, 8113 .

AMA Style

Sherif Ghoneim, Mohamed Kotb, Hany Hasanien, Mosleh Alharthi, Attia El-Fergany. Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis. Sustainability. 2021; 13 (14):8113.

Chicago/Turabian Style

Sherif Ghoneim; Mohamed Kotb; Hany Hasanien; Mosleh Alharthi; Attia El-Fergany. 2021. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis." Sustainability 13, no. 14: 8113.

Research article
Published: 28 June 2021 in International Journal of Energy Research
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The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method “Chaotic MayFly optimization algorithm” (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I–V characteristics of the PEMFC's are non-linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non-linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.

ACS Style

Mohamed A. M. Shaheen; Hany M. Hasanien; M. S. El Moursi; Attia A. El‐Fergany. Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm. International Journal of Energy Research 2021, 1 .

AMA Style

Mohamed A. M. Shaheen, Hany M. Hasanien, M. S. El Moursi, Attia A. El‐Fergany. Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm. International Journal of Energy Research. 2021; ():1.

Chicago/Turabian Style

Mohamed A. M. Shaheen; Hany M. Hasanien; M. S. El Moursi; Attia A. El‐Fergany. 2021. "Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm." International Journal of Energy Research , no. : 1.

Journal article
Published: 22 June 2021 in Energies
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Efficient and accurate estimations of unidentified parameters of photovoltaic (PV) models are essential to their simulation. This study suggests two new variants of the whale optimization algorithm (WOA) for identifying the nine parameters of the three-diode PV model. The first variant abbreviated as RWOA is based on integrating the WOA with ranking methods under a novel updating scheme to utilize each whale within the population as much as possible during the optimization process. The second variant, namely HWOA, has been based on employing a novel cyclic exploration-exploitation operator with the RWOA to promote its local and global search for averting stagnation into local minima and accelerating the convergence speed in the right direction of the near-optimal solution. Experimentally, RWOA and HWOA are validated on a solar cell (RTC France) and two PV modules (Photowatt-PWP201 and Kyocera KC200GT). Further, these proposed variants are compared with five well-known parameter extraction models in order to demonstrate their notable advantages over the other existing competing algorithms for minimizing the root mean squared error (RMSE) between experimentally measured data and estimated one. The experimental findings show that RWOA is superior in some observed cases and superior in the other cases in terms of final accuracy and convergence speed; yet, HWOA is superior in all cases.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Sameh Askar; Mohamed Abouhawwash. Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations. Energies 2021, 14, 3729 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Sameh Askar, Mohamed Abouhawwash. Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations. Energies. 2021; 14 (13):3729.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Sameh Askar; Mohamed Abouhawwash. 2021. "Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations." Energies 14, no. 13: 3729.

Journal article
Published: 11 June 2021 in Energy
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Optimum modeling of the proton exchange membrane fuel cell (PEMFC) has attracted considerable research over the last decades to simulate, control, evaluate, manage, and optimize the performance of PEMFC stacks. The main problem in optimal modeling is that the model parameters are not provided by manufacturers, and the empirical dataset points are not sufficient to accurately model the cell. Therefore, a new approach based on the improved chimp optimization algorithm (IChOA) is proposed to define the uncertain parameters of the PEMFC. A ranking-based updating strategy and a balanced exploration and exploitation strategy (BEES) are employed here within the IChOA. In the first strategy, the unbeneficial solutions in the population are replaced with other solutions covering other regions, which are unreachable by the original one. The second strategy aims at utilizing iteration as much as possible so that, at the beginning, the method maximizes the exploration operator in the first half of the optimization process to ensure the balance between the exploration and exploitation framework; and then, in the second half, the exploitation capability is maximized attempting to find a better solution than the best-so-far. The proposed IChOA is validated by three well-known commercial PEMFCs, namely 250 W stack, Ballard Mark V, and AVISTA SR-12 500 W modular. The best results of the IChOA are compared with 15 nature-inspired metaheuristics algorithms and another one known as gradient-based optimizer under various statistical analyses and under varied operating conditions. The superiority of the IChOA is demonstrated in terms of convergence stability, and final accuracy.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Ripon K. Chakrabortty; Michael J. Ryan. Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis. Energy 2021, 233, 121096 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Ripon K. Chakrabortty, Michael J. Ryan. Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis. Energy. 2021; 233 ():121096.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "Adaptive and Efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis." Energy 233, no. : 121096.

Journal article
Published: 04 May 2021 in Renewable Energy
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In this article, a comprehensive detailed dynamic representation of multi-stacks fuel cells is addressed. This representation is applied and compromised for parallel connected and operated multi-stacks solid-oxide fuel cells (SOFCs), which is considered a hot topic and research challenge. A proposed control system is presented to manage SOFCs parallel stacks sharing and operation. A part of this technical challenge in regards to the parallel operation is to realize equal output voltages with various current values of each cell. An improved slime mould algorithm (ISMA) is proposed to set the operational parameters of parallel operated SOFCs stacks. Digital simulations have validated the proposed system effectiveness with optimal dynamic parameters under various operating scenarios. These scenarios include step-load changes (increase/decrease) under equal and unequal load sharing’s plus emergent fault conditions by short-circuit disturbance and protection tripping. Further validations thru comparisons of ISMA’s results with the best results of genetic algorithm (GA) as a well-established benchmark algorithm are emphasized. The simulation results show the efficacy of proposed technique and representation to improve the dynamic performance of the underlined system, for example at a particular scenario. Just to figure out the cropped results, it is noted the ability of proposed technique to realize a very smooth dynamic response (overshoot = 2.30e-5 pu and settling time = 0.054 s) compared to those achieved by GA (overshoot = 4.78e-2 pu and settling time = 0.22 s) under fuel cells different shares scenario.

ACS Style

Ahmed M. Othman; Attia A. El-Fergany. Optimal dynamic operation and modeling of parallel connected multi-stacks fuel cells with improved slime mould algorithm. Renewable Energy 2021, 175, 770 -782.

AMA Style

Ahmed M. Othman, Attia A. El-Fergany. Optimal dynamic operation and modeling of parallel connected multi-stacks fuel cells with improved slime mould algorithm. Renewable Energy. 2021; 175 ():770-782.

Chicago/Turabian Style

Ahmed M. Othman; Attia A. El-Fergany. 2021. "Optimal dynamic operation and modeling of parallel connected multi-stacks fuel cells with improved slime mould algorithm." Renewable Energy 175, no. : 770-782.

Journal article
Published: 28 April 2021 in Mathematics
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To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Mohamed Abouhawwash; S. Askar. Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm. Mathematics 2021, 9, 995 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Attia El-Fergany, Mohamed Abouhawwash, S. Askar. Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm. Mathematics. 2021; 9 (9):995.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Attia El-Fergany; Mohamed Abouhawwash; S. Askar. 2021. "Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm." Mathematics 9, no. 9: 995.

Journal article
Published: 27 March 2021 in Energies
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The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Ripon Chakrabortty; Michael Ryan; Attia El-Fergany. An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models. Energies 2021, 14, 1867 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Ripon Chakrabortty, Michael Ryan, Attia El-Fergany. An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models. Energies. 2021; 14 (7):1867.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Ripon Chakrabortty; Michael Ryan; Attia El-Fergany. 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models." Energies 14, no. 7: 1867.

Original article
Published: 17 March 2021 in Neural Computing and Applications
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In this paper, a new stochastic optimizer called slime mould algorithm (SMA) for solving the optimal coordination of directional overcurrent relays in meshed power networks is proposed. The proposed SMA-based method deals with the coordination problem using the most recent digital relays by selecting the optimal tripping standardized curve plus time dial and current pickup. Afterwards, the problem gets more sophisticated by bounding the relay tripping time inside a practical range to simulate the real network conditions. SMA’s performance is evaluated on two different test cases including highly penetrated distributed generators in the second one. The results of SMA are compared with other recent metaheuristic and conventional approaches reported in the literature under same conditions aimed at fair comparisons. It is declared that the SMA attains 29.9% reduction in the relays total operating time for the first test case compared to the well-matured water cycle algorithm. Even, if the tested network is a highly penetrated by distributed generators, more reduction is achieved by 33.7% with better convergence characteristics. It can be established that the SMA-based method demonstrates a competitive and reliable tool for minimizing the relays total operating time satisfying all the constraints even for the sophisticated scenarios.

ACS Style

Abdelmonem Draz; Mahmoud M. Elkholy; Attia A. El-Fergany. Slime mould algorithm constrained by the relay operating time for optimal coordination of directional overcurrent relays using multiple standardized tripping curves. Neural Computing and Applications 2021, 33, 11875 -11887.

AMA Style

Abdelmonem Draz, Mahmoud M. Elkholy, Attia A. El-Fergany. Slime mould algorithm constrained by the relay operating time for optimal coordination of directional overcurrent relays using multiple standardized tripping curves. Neural Computing and Applications. 2021; 33 (18):11875-11887.

Chicago/Turabian Style

Abdelmonem Draz; Mahmoud M. Elkholy; Attia A. El-Fergany. 2021. "Slime mould algorithm constrained by the relay operating time for optimal coordination of directional overcurrent relays using multiple standardized tripping curves." Neural Computing and Applications 33, no. 18: 11875-11887.

Journal article
Published: 06 February 2021 in Energy Reports
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The characterization of PV unit can be made using one-, two-, and triple-diode electrical model. Each model has its own merits in terms of number of unknown parameters to be extracted and burden of calculation, and etc. In most applications, one-diode model (1-DM) is sufficed for the purpose of simulation and analysis in steady-state and dynamic conditions. This paper cares of extracting the unknown five parameters of the 1-DM exploiting the real I-V dataset points. New effort of employing the slime mould algorithm (SMA) and its improved version (ImSMA) is addressed to attain the same goal. Lambert W-function or omega function is used for accurate calculus of PV current. Two benchmarking test cases widely used in the literature are demonstrated and analysed to appraise the performance of SMA/ImSMA complete with subsequent analysis and discussions. The best ImSMA’s results of root mean squared current errors are 7.73006e−4 A and 1.3798e−2 A for RTC solar cell and STP6-120/36; respectively. Various scenarios under varied conditions are demonstrated utilizing the cropped best values of the model’s parameters In addition to that, performance measures are made to validate the cropped results along with comparisons to other recent competing algorithms. It can be concluded that the validations in consort with established outcomes signify the ImSMA in recognizing the PV unidentified 1-DM parameters.

ACS Style

Attia A. El-Fergany. Parameters identification of PV model using improved slime mould optimizer and Lambert W-function. Energy Reports 2021, 7, 875 -887.

AMA Style

Attia A. El-Fergany. Parameters identification of PV model using improved slime mould optimizer and Lambert W-function. Energy Reports. 2021; 7 ():875-887.

Chicago/Turabian Style

Attia A. El-Fergany. 2021. "Parameters identification of PV model using improved slime mould optimizer and Lambert W-function." Energy Reports 7, no. : 875-887.

Journal article
Published: 13 January 2021 in Energy
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In this paper, a novel attempt to employ the Jellyfish search algorithm (JSA) for solving parameters’ identifications problem of polymer exchange membrane fuel cells (PEMFCs) model is addressed. The minimization of the sum of squared errors (SSEs) between measured and estimated voltage dataset points define the fitness function to be optimized by the JSA subject to set of self-constrained inequality bounds. Three test cases are demonstrated complete with necessary verifications, comparisons and subsequent discussions. It can be quantified here that the best cropped values of SSEs are equal to 0.011699, 0.33598 and 2.14570 V2 for BCS 500-W module, 250-W stack and NedStack type PS6 unit; respectively. It can be confirmed that the maximum value of percentage voltage biased error is ±1% for all test cases under study. In addition, various performance measures are made to signify the robustness of the cropped results. At a later stage, various operating principal characteristics under changeable temperatures and varied regulating pressures are illustrated. It can be established that the JSA proves its ability to tackle this problem competently compared to others.

ACS Style

Eid A. Gouda; Mohamed F. Kotb; Attia A. El-Fergany. Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis. Energy 2021, 221, 119836 .

AMA Style

Eid A. Gouda, Mohamed F. Kotb, Attia A. El-Fergany. Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis. Energy. 2021; 221 ():119836.

Chicago/Turabian Style

Eid A. Gouda; Mohamed F. Kotb; Attia A. El-Fergany. 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis." Energy 221, no. : 119836.

Original research paper
Published: 12 January 2021 in IET Renewable Power Generation
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Efficient modelling of photovoltaic (PV) generating units' characteristics to investigate their steady‐state and dynamic impacts on the performances of power systems and electric drives is essential. The current work aims at developing an effective tool based on artificial ecosystem optimiser (AEO) to define (optimally) the uncertain parameters of PV generating units. The root mean squared deviations (RMSDs) along with the predefined inequality constraints formulate the optimization problem to be solved by the AEO. Initially, two test cases with different PV technologies are demonstrated complete with their relevant discussions and necessary validations. At a later stage, real measurements (followed the procedures of IEC 60904) of a commercial PV module namely Ultra 85‐P of Shell PowerMax are made for further experimental validations of the AEO results. Various operating temperatures and sun irradiance levels are investigated among the simulated scenarios. The statistical validations along with predefined indices plus comparisons to other competing methods appraise the results obtained by the AEO. It can be confirmed that the AEO is able to produce best values of unidentified parameters of the PV units with different solar technologies under study with lesser values of RMSDs among other optimisers.

ACS Style

Mahmoud M. Elkholy; Mohamed A. El‐Hameed; Attia A. El‐Fergany. Artificial ecosystem‐based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations. IET Renewable Power Generation 2021, 15, 701 -715.

AMA Style

Mahmoud M. Elkholy, Mohamed A. El‐Hameed, Attia A. El‐Fergany. Artificial ecosystem‐based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations. IET Renewable Power Generation. 2021; 15 (3):701-715.

Chicago/Turabian Style

Mahmoud M. Elkholy; Mohamed A. El‐Hameed; Attia A. El‐Fergany. 2021. "Artificial ecosystem‐based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations." IET Renewable Power Generation 15, no. 3: 701-715.

Journal article
Published: 08 January 2021 in Renewable Energy
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The representative model of fuel cells (FCs) has a vital role in investigating the steady-state and dynamic analysis of these cells. The FC model has a significant impact on simulation studies of such systems and this is appeared in several applications like nano grids, microgrids and smart grids. This manuscript attempts a novel and simplified precise model of the proton electrolyte membrane FCs (PEMFCs). The proposed model deeply reduces the number of unknown parameters of such models to achieve a simplified one. In this regard, it reveals only four design variables within the model. Another salient feature of this study is the novel application of a meta-heuristic algorithm called equilibrium optimizer (EO) to obtain these unknown four parameters. The integral squared error criteria is used to formulate the fitness function. The effectiveness of EO-PEMFC model is tested by comparing the simulation with experimental results under various operating situations. The validity of proposed model is also verified by benchmarking its results with that obtained using other optimization-based models. The high efficacy of this model is checked under both steady-state and dynamic operating conditions. With the application of the proposed model, a very precise PEMFC model can be implemented.

ACS Style

Sameh I. Seleem; Hany M. Hasanien; Attia A. El-Fergany. Equilibrium optimizer for parameter extraction of a fuel cell dynamic model. Renewable Energy 2021, 169, 117 -128.

AMA Style

Sameh I. Seleem, Hany M. Hasanien, Attia A. El-Fergany. Equilibrium optimizer for parameter extraction of a fuel cell dynamic model. Renewable Energy. 2021; 169 ():117-128.

Chicago/Turabian Style

Sameh I. Seleem; Hany M. Hasanien; Attia A. El-Fergany. 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model." Renewable Energy 169, no. : 117-128.

Journal article
Published: 01 January 2021 in DYNA
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The accuracy of fuel cell (FC) models is important for the further numerical simulations and analysis at several conditions. The electrical (I-V) characteristic of the polymer exchange membrane fuel cells (PEMFCs) has high degree of nonlinearity comprising uncertain seven parameters as they aren’t given in fabricator's datasheets. These seven parameters need to be obtained to have the PEMFC model in order. This research addresses an up-to-date application of the gradient-based optimizer (GBO) to generate the best estimated values of such uncertain parameters. The estimation of these uncertain parameters is adapted as optimization problem having a cost function (CF) subjects to set of self-constrained limits. Three test cases of widely used PEMFCs units; namely, SR-12, 250-W module and NedStack PS6 to appraise the performance of the GBO are demonstrated and analyzed. The best values of the CF are 0.000142, 0.33598, and 2.10025 V2 for SR-12, 250-W module and NedStack PS6; respectively. Furthermore, the assessment of the GBO-based model is made by comparing its obtained results with the experiential results of these typical PEMFCs plus comparisons to other methods. At a due stage, many scenarios as a result of operating variations in regard to inlet regulation pressures and unit temperatures are performed. The copped reported results of the studied scenarios indicate the effectiveness of the GBO in establishing an accurate PEMFC model.

ACS Style

Salah Kamal; Attia El-Fergany; Ehab Ehab Elsayed Elattar; Ahmed Agwa. STEADY-STATE MODELLING OF PEM FUEL CELLS USING GRADIENT-BASED OPTIMIZER. DYNA 2021, DYNA-ACELE, [ 8 pp.] -[ 8 pp.].

AMA Style

Salah Kamal, Attia El-Fergany, Ehab Ehab Elsayed Elattar, Ahmed Agwa. STEADY-STATE MODELLING OF PEM FUEL CELLS USING GRADIENT-BASED OPTIMIZER. DYNA. 2021; DYNA-ACELE ():[ 8 pp.]-[ 8 pp.].

Chicago/Turabian Style

Salah Kamal; Attia El-Fergany; Ehab Ehab Elsayed Elattar; Ahmed Agwa. 2021. "STEADY-STATE MODELLING OF PEM FUEL CELLS USING GRADIENT-BASED OPTIMIZER." DYNA DYNA-ACELE, no. : [ 8 pp.]-[ 8 pp.].

Journal article
Published: 19 October 2020 in Energy Conversion and Management
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This paper presents a novel conscious neighborhood strategy-based Laplacian barnacles mating algorithm, named NLBMA for solving solar cell diode models. It is an indispensable and prudent improvement to address insufficient diversification and intensification inclinations and the entrapment in the local optima of the original barnacles mating algorithm (BMA). In this regard, the proposed NLBMA introduces two new searching methodologies called Laplacian-based crossover search (LCS) and neighborhood-based wandering search (NWS) in order to boost the searching ability of barnacles by exploring different regions within the search space. In this sense, the LCS aims to improve the diversification of solutions while the NWS operates in enhancing the intensification ability by refining the solution quality and then the balancing between local and global searches can be enhanced consciously. The proposed NLBMA is used to optimize the parameters of single-diode (SD), and double-diode (DD) models of photovoltaic (PV) units. In all experiments, NLBMA is compared with the standard BMA and different state-of-the-art recent optimization algorithms. The comprehensive and statistical results indicate that NLBMA is more accurate and superior compared to other peers, where for all the studies PV models/modules, the NLBMA has proved to consistently achieve improved results for the root mean squared error (RMSE) of current than the others. For example, the results of RMSE found by NLBMA for R.T.C. France silicon solar cell are 7.73006e−4 A for SD model and 7.5242e−4 A for DD model, with percentage of improvement 83.23% and 90.94% on the original BMA variant, respectively. For the KC200GT module, the found RMSE values are 1.9827e−3 A and 2.0083e−3 A with saving 1.48% and 96.27% for the SD and DD modules over the original BMA variant, respectively. For the Photowatt PWP-201 module, the results are 3.3610e−2 A and 3.3043e−2 A with improvement 67.19% and 73.55% for the SD and DD models compared to the original BMA variant, respectively. Therefore, the NLBMA can efficiently prove its superiority and reliability to tackle the problem of parameters’ extraction of the PV models.

ACS Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. Conscious neighborhood scheme-based Laplacian barnacles mating algorithm for parameters optimization of photovoltaic single- and double-diode models. Energy Conversion and Management 2020, 226, 113522 .

AMA Style

Rizk M. Rizk-Allah, Attia A. El-Fergany. Conscious neighborhood scheme-based Laplacian barnacles mating algorithm for parameters optimization of photovoltaic single- and double-diode models. Energy Conversion and Management. 2020; 226 ():113522.

Chicago/Turabian Style

Rizk M. Rizk-Allah; Attia A. El-Fergany. 2020. "Conscious neighborhood scheme-based Laplacian barnacles mating algorithm for parameters optimization of photovoltaic single- and double-diode models." Energy Conversion and Management 226, no. : 113522.

Original article
Published: 02 September 2020 in Neural Computing and Applications
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In this article, a control scheme based on chicken swarm optimizer (CSO) in cooperation with adaptive virtual-inertia control (AVIC) is investigated. The proposed control scheme aims at improving the frequency stability of an interconnected power system which is penetrated by renewable energy sources. The CSO is applied to produce the best values of the gains of the adapted standard proportional-integral-derivative (PID) controllers and required parameters of AVICs. Various scenarios are addressed in this study such as applications of sudden step load disturbances and severe variations in the inertia of the system. In addition, realistic conditions such as uncertainties of tidal power source and random load disturbances are demonstrated. Compulsory assessments with subsequent discussions to evaluate the results of the CSO are made. The proposed CSO–AVIC based control method is verified by comparisons with well-matured interesting algorithms such as differential evolution and particle swarm optimizers. Various quality specifications of the dynamic responses and the demonstrated results indicate clearly the viability of the proposed CSO–AVIC based on control scheme. It can be emphasized that the utilization of AVIC along with PID controllers are significantly improved the system dynamic performances and their dynamic response specifications meet the terms of standard acceptable criteria’s.

ACS Style

Ahmed M. Othman; Attia A. El-Fergany. Adaptive virtual-inertia control and chicken swarm optimizer for frequency stability in power-grids penetrated by renewable energy sources. Neural Computing and Applications 2020, 33, 2905 -2918.

AMA Style

Ahmed M. Othman, Attia A. El-Fergany. Adaptive virtual-inertia control and chicken swarm optimizer for frequency stability in power-grids penetrated by renewable energy sources. Neural Computing and Applications. 2020; 33 (7):2905-2918.

Chicago/Turabian Style

Ahmed M. Othman; Attia A. El-Fergany. 2020. "Adaptive virtual-inertia control and chicken swarm optimizer for frequency stability in power-grids penetrated by renewable energy sources." Neural Computing and Applications 33, no. 7: 2905-2918.

Research article
Published: 18 August 2020 in International Journal of Energy Research
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This paper addresses a new attempt of the AEFA to define the uncertain model parameters of TDM of PV units. Two commercial PV modules are investigated with intensive simulations and necessary analysis. The parameters of AFEA based TDM are validated thru the empirical dataset points. Necessary performance assessments are made which signify the AEFA results compared to others. Dynamic simulations of MPP is performed.

ACS Style

Sameh I. Selem; Attia A. El‐Fergany; Hany M. Hasanien. Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model. International Journal of Energy Research 2020, 45, 590 -604.

AMA Style

Sameh I. Selem, Attia A. El‐Fergany, Hany M. Hasanien. Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model. International Journal of Energy Research. 2020; 45 (1):590-604.

Chicago/Turabian Style

Sameh I. Selem; Attia A. El‐Fergany; Hany M. Hasanien. 2020. "Artificial electric field algorithm to extract nine parameters of triple‐diode photovoltaic model." International Journal of Energy Research 45, no. 1: 590-604.