12 results on '"Moustafa, Ghareeb"'
Search Results
2. A Proportional-Integral-One Plus Double Derivative Controller-Based Fractional-Order Kepler Optimizer for Frequency Stability in Multi-Area Power Systems with Wind Integration.
- Author
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Alqahtani, Mohammed H., Almutairi, Sulaiman Z., Aljumah, Ali S., Shaheen, Abdullah M., Moustafa, Ghareeb, and El-Fergany, Attia A.
- Subjects
WIND power ,FREQUENCY stability ,EVOLUTIONARY computation ,RENEWABLE energy sources ,WIND power plants - Abstract
This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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3. Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem.
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Hakmi, Sultan Hassan, Alnami, Hashim, Moustafa, Ghareeb, Ginidi, Ahmed R., and Shaheen, Abdullah M.
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DIFFERENTIAL operators ,OPTIMIZATION algorithms ,PARAMETER estimation ,POLYNOMIALS ,PHENOMENOLOGICAL theory (Physics) - Abstract
A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize the physical phenomenon of rime-ice growth. It simulates the hard-rime and soft-rime processes, constructing the mechanisms of hard-rime puncture and soft-rime search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation of PDLO introduces non-linearities to the RIME algorithm, enhancing its adaptability, convergence speed, and global search capability compared to the conventional RIME approach. The proposed MRIME algorithm is designed to identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including the One-Diode Model (ONE-DM) and Two-Diode Model TWO-DM, to determine the unspecified parameters of the PV. The MRIME approach is compared to the conventional RIME method using two commercial PV modules, namely the STM6-40/36 module and R.T.C. France cell. The simulation results are juxtaposed with those from contemporary algorithms based on published research. The outcomes related to recent algorithms are also compared with those of the MRIME algorithm in relation to various existing studies. The simulation results indicate that the MRIME algorithm demonstrates substantial improvement rates for the STM6-40/36 module and R.T.C. France cell, achieving 1.16% and 18.45% improvement for the ONE-DM, respectively. For the TWO-DM, it shows significant improvement rates for the two modules, reaching 1.14% and 50.42%, respectively. The MRIME algorithm, in comparison to previously published results, establishes substantial superiority and robustness. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction.
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Moustafa, Ghareeb, Smaili, Idris H., Almalawi, Dhaifallah R., Ginidi, Ahmed R., Shaheen, Abdullah M., Elshahed, Mostafa, and Mansour, Hany S. E.
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PHOTOVOLTAIC power systems ,MONGOOSES ,SOLAR system ,STANDARD deviations - Abstract
This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO's primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO's success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. Kepler Algorithm for Large-Scale Systems of Economic Dispatch with Heat Optimization.
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Hakmi, Sultan Hassan, Shaheen, Abdullah M., Alnami, Hashim, Moustafa, Ghareeb, and Ginidi, Ahmed
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OPTIMIZATION algorithms ,ECONOMIC systems ,PARTICLE swarm optimization ,ALGORITHMS ,FUEL costs - Abstract
Combined Heat and Power Units Economic Dispatch (CHPUED) is a challenging non-convex optimization challenge in the power system that aims at decreasing the production cost by scheduling the heat and power generation outputs to dedicated units. In this article, a Kepler optimization algorithm (KOA) is designed and employed to handle the CHPUED issue under valve points impacts in large-scale systems. The proposed KOA is used to forecast the position and motion of planets at any given time based on Kepler's principles of planetary motion. The large 48-unit, 96-unit, and 192-unit systems are considered in this study to manifest the superiority of the developed KOA, which reduces the fuel costs to 116,650.0870 USD/h, 234,285.2584 USD/h, and 487,145.2000 USD/h, respectively. Moreover, the dwarf mongoose optimization algorithm (DMOA), the energy valley optimizer (EVO), gray wolf optimization (GWO), and particle swarm optimization (PSO) are studied in this article in a comparative manner with the KOA when considering the 192-unit test system. For this large-scale system, the presented KOA successfully achieves improvements of 19.43%, 17.49%, 39.19%, and 62.83% compared to the DMOA, the EVO, GWO, and PSO, respectively. Furthermore, a feasibility study is conducted for the 192-unit test system, which demonstrates the superiority and robustness of the proposed KOA in obtaining all operating points between the boundaries without any violations. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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6. An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters.
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Moustafa, Ghareeb, Alnami, Hashim, Hakmi, Sultan Hassan, Ginidi, Ahmed, Shaheen, Abdullah M., and Al-Mufadi, Fahad A.
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SEARCH algorithms , *GLOBAL optimization , *OPTIMIZATION algorithms , *SOLAR energy conversion , *MANTODEA , *SOLAR cell efficiency - Abstract
Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations of PV panels, necessitating the development of creative approaches to improve solar energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition to get trapped in certain local optima. This paper develops the Mantis Search Algorithm (MSA), which draws inspiration from the unique foraging behaviours and sexual cannibalism of praying mantises. The suggested MSA includes three stages of optimisation: prey pursuit, prey assault, and sexual cannibalism. It is created for the R.TC France PV cell and the Ultra 85-P PV panel related to Shell PowerMax for calculating PV parameters and examining six case studies utilising the one-diode model (1DM), two-diode model (1DM), and three-diode model (3DM). Its performance is assessed in contrast to recently developed optimisers of the neural network optimisation algorithm (NNA), dwarf mongoose optimisation (DMO), and zebra optimisation algorithm (ZOA). In light of the adopted MSA approach, simulation findings improve the electrical characteristics of solar power systems. The developed MSA methodology improves the 1DM, 2DM, and 3DM by 12.4%, 44.05%, and 48.88%, 28.96%, 43.19%, and 55.81%, 37.71%, 32.71%, and 60.13% relative to the DMO, NNA, and ZOA approaches, respectively. For the Ultra 85-P PV panel, the designed MSA technique achieves improvements for the 1DM, 2DM, and 3DM of 62.05%, 67.14%, and 84.25%, 49.05%, 53.57%, and 74.95%, 37.03%, 37.4%, and 59.57% compared to the DMO, NNA, and ZOA techniques, respectively. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer.
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Moustafa, Ghareeb
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PHOTOVOLTAIC power systems , *OPTIMIZATION algorithms , *SOLAR panels , *SOLAR technology , *SYSTEM identification , *PERFORMANCE management - Abstract
Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms. [ABSTRACT FROM AUTHOR]
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- 2023
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8. A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems.
- Author
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Moustafa, Ghareeb, Tolba, Mohamed A., El-Rifaie, Ali M., Ginidi, Ahmed, Shaheen, Abdullah M., and Abid, Slim
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ELECTRIC power distribution grids , *OPTIMIZATION algorithms , *PROBLEM solving , *THYRISTOR control , *MATHEMATICAL functions - Abstract
The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA's simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems.
- Author
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Moustafa, Ghareeb, El-Rifaie, Ali M., Smaili, Idris H., Ginidi, Ahmed, Shaheen, Abdullah M., Youssef, Ahmed F., and Tolba, Mohamed A.
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OPTIMIZATION algorithms , *MONGOOSES , *PROBLEM solving , *FORAGING behavior , *MATHEMATICAL functions - Abstract
This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA's core concept is inspired by the dwarf mongoose's foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
10. Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules.
- Author
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Aribia, Houssem Ben, El-Rifaie, Ali M., Tolba, Mohamed A., Shaheen, Abdullah, Moustafa, Ghareeb, Elsayed, Fahmi, and Elshahed, Mostafa
- Abstract
One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans' learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one's weaknesses and altering one's learning techniques to aid in one's improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO's performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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11. Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems.
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Abid, Slim, El-Rifaie, Ali M., Elshahed, Mostafa, Ginidi, Ahmed R., Shaheen, Abdullah M., Moustafa, Ghareeb, and Tolba, Mohamed A.
- Subjects
MYXOMYCETES ,FREQUENCY stability ,OPTIMIZATION algorithms ,PID controllers ,ELECTRICAL load - Abstract
Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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12. A Gradient-Based Optimizer with a Crossover Operator for Distribution Static VAR Compensator (D-SVC) Sizing and Placement in Electrical Systems.
- Author
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Moustafa, Ghareeb, Elshahed, Mostafa, Ginidi, Ahmed R., Shaheen, Abdullah M., and Mansour, Hany S. E.
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STATIC VAR compensators , *REACTIVE power , *METAHEURISTIC algorithms , *DIFFERENTIAL evolution , *ENERGY dissipation , *MONGOOSES - Abstract
A gradient-based optimizer (GBO) is a recently inspired meta-heuristic technique centered on Newton's gradient-based approach. In this paper, an advanced developed version of the GBO is merged with a crossover operator (GBOC) to enhance the diversity of the created solutions. The merged crossover operator causes the solutions in the next generation to be more random. The proposed GBOC maintains the original Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR directs the search to potential areas and aids in its convergence to the optimal answer, while the LEO aids the searching process in avoiding local optima. The proposed GBOC technique is employed to optimally place and size the distribution static VAR compensator (D-SVC), one of the distribution flexible AC transmission devices (D-FACTS). It is developed to maximize the yearly energy savings via power losses concerning simultaneously different levels of the peak, average, and light loadings. Its relevance is tested on three distribution systems of IEEE 33, 69, and 118 nodes. Based on the proposed GBOC, the outputs of the D-SVCs are optimally varying with the loading level. Furthermore, their installed ratings are handled as an additional constraint relating to two compensation levels of 50% and 75% of the total reactive power load to reflect a financial installation limit. The simulation applications of the proposed GBOC declare great economic savings in yearly energy losses for the three distribution systems with increasing compensation levels and iterations compared to the initial case. In addition, the effectiveness of the proposed GBOC is demonstrated compared to several techniques, such as the original GBO, the salp swarm algorithm, the dwarf mongoose algorithm, differential evolution, and honey badger optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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