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Dr. Sherif Ghoneim
Associate professor, Department of Electrical Engineering, College of Engineering

Basic Info


Research Keywords & Expertise

0 dielectric breakdown
0 grounding systems
0 Insulation diagnosis
0 artificial intelligence
0 dissolved gas analysis

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dissolved gas analysis
artificial intelligence
Box-Behnken design
dielectric breakdown

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Short Biography

Sherif S. M. Ghoneim (SMIEEE) received his B.Sc. and M.Sc. degrees from the Faculty of Engineering at Shoubra, Zagazig University, Egypt, in 1994 and 2000, respectively. Since 1996, he has been teaching at the Faculty of Industrial Education, Suez Canal University, Egypt. From the end of 2005 to the end of 2007, he was a guest researcher at the Institute of Energy Transport and Storage (ETS) of the University of Duisburg–Essen in Germany. In 2008, he earned his Ph.D. degree in electrical power and machines from the Faculty of Engineering, Cairo University (2008). He joined Taif University as an Associate Professor in the Electrical Engineering Department, Faculty of Engineering. His research areas include grounding systems, dissolved gas analysis, breakdown in SF6 gas, and AI technique applications.

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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.

Journal article
Published: 22 August 2021 in Applied Sciences
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This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.

ACS Style

Habib Kraiem; Flah Aymen; Lobna Yahya; Alicia Triviño; Mosleh Alharthi; Sherif S. M. Ghoneim. A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System. Applied Sciences 2021, 11, 7732 .

AMA Style

Habib Kraiem, Flah Aymen, Lobna Yahya, Alicia Triviño, Mosleh Alharthi, Sherif S. M. Ghoneim. A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System. Applied Sciences. 2021; 11 (16):7732.

Chicago/Turabian Style

Habib Kraiem; Flah Aymen; Lobna Yahya; Alicia Triviño; Mosleh Alharthi; Sherif S. M. Ghoneim. 2021. "A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System." Applied Sciences 11, no. 16: 7732.

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.

Journal article
Published: 13 July 2021 in Energy
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An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer (MOQRJFS) is presented in this article for solving multi-dimensional Optimal Power Flow (MDOPF) issue with diverse objectives which display the minimization of economic fuel cost, total emissions, and the active power loss with satisfying operational constraints. Despite the simple structure of JFS with control of exploitation and exploration, searching capability of the JFS requires more support. Hence, two modifications are performed on the standard JFS algorithm. the first modification is that a cluster with a random size has been proposed which illustrates the social community that can share the data in the cluster and are dissimilar from one to another. The second modification is that a quasi-opposition-based learning is emerged in JFS to support the exploration phase. As selection criteria for the best solutions, a fuzzy decision-making strategy is joint into MOQRJFS optimizer. Additionally, the Pareto optimality concept is added to extract the non-dominated solutions. The superiority of the MOQRJFS is proved throughout application on IEEE 30-bus system, IEEE 57-bus system, the West Delta Region System of 52 bus (WDRS-52) in Egypt, and a large scale 118-bus system. Thirteen cases with economic, environmental, and technical objectives of MDOPF are included in this study. The outcomes of the proposed MOQRJFS have been compared with the conventional MOJFS and the reported techniques in the literature. It is clearly observed that the MOQRJFS give the minimum values compared with these techniques which reveals its robustness, effectiveness, and superiority when handling MDOPF among other techniques.

ACS Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim; Ahmed R. Ginidi. Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework. Energy 2021, 237, 121478 .

AMA Style

Abdullah M. Shaheen, Ragab A. El-Sehiemy, Mosleh M. Alharthi, Sherif S.M. Ghoneim, Ahmed R. Ginidi. Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework. Energy. 2021; 237 ():121478.

Chicago/Turabian Style

Abdullah M. Shaheen; Ragab A. El-Sehiemy; Mosleh M. Alharthi; Sherif S.M. Ghoneim; Ahmed R. Ginidi. 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework." Energy 237, no. : 121478.

Journal article
Published: 25 June 2021 in Studies in Informatics and Control
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ACS Style

Mosleh Alharthi; Sherif Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Ahmed Ginidi. A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids. Studies in Informatics and Control 2021, 30, 89 -99.

AMA Style

Mosleh Alharthi, Sherif Ghoneim, Abdallah Elsayed, Ragab El-Sehiemy, Abdullah Shaheen, Ahmed Ginidi. A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids. Studies in Informatics and Control. 2021; 30 (2):89-99.

Chicago/Turabian Style

Mosleh Alharthi; Sherif Ghoneim; Abdallah Elsayed; Ragab El-Sehiemy; Abdullah Shaheen; Ahmed Ginidi. 2021. "A Multi-Objective Marine Predator Optimizer for Optimal Techno-Economic Operation of AC/DC Grids." Studies in Informatics and Control 30, no. 2: 89-99.

Journal article
Published: 25 June 2021 in Sensors
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Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system’s effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils’ position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation.

ACS Style

Naoui Mohamed; Flah Aymen; Zaafouri Issam; Mohit Bajaj; Sherif Ghoneim; Mahrous Ahmed. The Impact of Coil Position and Number on Wireless System Performance for Electric Vehicle Recharging. Sensors 2021, 21, 4343 .

AMA Style

Naoui Mohamed, Flah Aymen, Zaafouri Issam, Mohit Bajaj, Sherif Ghoneim, Mahrous Ahmed. The Impact of Coil Position and Number on Wireless System Performance for Electric Vehicle Recharging. Sensors. 2021; 21 (13):4343.

Chicago/Turabian Style

Naoui Mohamed; Flah Aymen; Zaafouri Issam; Mohit Bajaj; Sherif Ghoneim; Mahrous Ahmed. 2021. "The Impact of Coil Position and Number on Wireless System Performance for Electric Vehicle Recharging." Sensors 21, no. 13: 4343.

Original article
Published: 15 June 2021 in Journal of Electrical Engineering & Technology
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This paper shows an application of hybrid PV/wind energy and battery storage in the islanded area. This work’s main target allows the distributed energy resources to contribute efficiently in the economic feasibility and enhance the environmental impact of the hybrid renewable energy source. Several factors such as levelized cost of energy (COE), greenhouse gas (GHG) emissions, and loss of power supply probability are studied. A combined solution is to compromise the economic and environmental aspects via the Utopia point approach is investigated. The optimal configuration of the hybrid PV/wind along with battery-storage and diesel engine as secondary source is obtained via meta-heuristic optimizers: Genetic Algorithm (GA) and Particle-Swarm Optimization (PSO) and impartial comparison of the results with HOMER software. The results of Utopia point solution show that the PV (about 46%) and wind turbine (about 13%) are shared significantly as primary renewable sources and battery storage (about 39%) as storage options. Meanwhile, the diesel engine (about 3%) has insignificant sharing in feeding the demand load. The optimal COE and GHG, which are achieved via GA and PSO optimization techniques are 0.182$/kWh and 12076 kg/year, agansit 0.343$/kWh and 17618 kg/year that are obtained via HOMER software, respectively. This corssponing to 47% and 31% reduction in COE and GHG, respectively. Sensitivity studies demonstrate that the variation of the wind energy sharing from 50 to 150% shows that the wind energy has a slight effect considering the GHG emissions. Contrarily, lower PV sharing ratios undesirably raises the levelized COE, however, reduces the GHG emissions.

ACS Style

Ahmed Elnozahy; Ali M. Yousef; Sherif S. M. Ghoneim; Saad A. Mohamed Abdelwahab; Moayed Mohamed; Farag K. Abo-Elyousr. Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System. Journal of Electrical Engineering & Technology 2021, 1 -16.

AMA Style

Ahmed Elnozahy, Ali M. Yousef, Sherif S. M. Ghoneim, Saad A. Mohamed Abdelwahab, Moayed Mohamed, Farag K. Abo-Elyousr. Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System. Journal of Electrical Engineering & Technology. 2021; ():1-16.

Chicago/Turabian Style

Ahmed Elnozahy; Ali M. Yousef; Sherif S. M. Ghoneim; Saad A. Mohamed Abdelwahab; Moayed Mohamed; Farag K. Abo-Elyousr. 2021. "Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System." Journal of Electrical Engineering & Technology , no. : 1-16.

Journal article
Published: 04 June 2021 in IEEE Access
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Dissolved gas analysis (DGA) is the standard technique to diagnose the fault types of oil-immersed power transformers. Various traditional DGA methods have been employed to detect the transformer faults, but their accuracies were mostly poor. In this light, the current work aims to improve the diagnostic accuracy of power transformer faults using artificial intelligence. A KNN algorithm is combined with the decision tree principle as an improved DGA diagnostic tool. A total of 501 dataset samples are used to train and test the proposed model. Based on the number of correct detections, the neighbor’s number and distance type of the KNN algorithm are optimized in order to improve the classifier’s accuracy rate. For each fault, indeed, several input vectors are assessed to select the most appropriate one for the classifier’s corresponding layer, increasing the overall diagnostic accuracy. On the basis of the accuracy rate obtained by knots and type of defect, two models are proposed where their results are compared and discussed. It is found that the global accuracy rate exceeds 93% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique. An independent database is employed as a complimentary validation phase of the proposed research.

ACS Style

Omar Kherif; Youcef Benmahamed; Madjid Teguar; Ahmed Boubakeur; Sherif S. M. Ghoneim. Accuracy Improvement of Power Transformer Faults Diagnostic Using KNN Classifier With Decision Tree Principle. IEEE Access 2021, 9, 81693 -81701.

AMA Style

Omar Kherif, Youcef Benmahamed, Madjid Teguar, Ahmed Boubakeur, Sherif S. M. Ghoneim. Accuracy Improvement of Power Transformer Faults Diagnostic Using KNN Classifier With Decision Tree Principle. IEEE Access. 2021; 9 ():81693-81701.

Chicago/Turabian Style

Omar Kherif; Youcef Benmahamed; Madjid Teguar; Ahmed Boubakeur; Sherif S. M. Ghoneim. 2021. "Accuracy Improvement of Power Transformer Faults Diagnostic Using KNN Classifier With Decision Tree Principle." IEEE Access 9, no. : 81693-81701.

Journal article
Published: 01 June 2021 in Processes
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The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

ACS Style

Manzar Nezami; Danish Equbal; Shakeb Khan; Shiraz Sohail; Sherif Ghoneim. Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA). Processes 2021, 9, 981 .

AMA Style

Manzar Nezami, Danish Equbal, Shakeb Khan, Shiraz Sohail, Sherif Ghoneim. Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA). Processes. 2021; 9 (6):981.

Chicago/Turabian Style

Manzar Nezami; Danish Equbal; Shakeb Khan; Shiraz Sohail; Sherif Ghoneim. 2021. "Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA)." Processes 9, no. 6: 981.

Journal article
Published: 25 May 2021 in IEEE Access
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Detection of transformer faults avoids the transformer’s undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers’ ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques’ parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon’s rank-sum, confirms the algorithm’s accuracy.

ACS Style

Sherif S. M. Ghoneim; Tamer Ahmed Farrag; A. Ali Rashed; El-Sayed M. El-Kenawy; Abdelhameed Ibrahim. Adaptive Dynamic Meta-heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults. IEEE Access 2021, 9, 1 -1.

AMA Style

Sherif S. M. Ghoneim, Tamer Ahmed Farrag, A. Ali Rashed, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim. Adaptive Dynamic Meta-heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Sherif S. M. Ghoneim; Tamer Ahmed Farrag; A. Ali Rashed; El-Sayed M. El-Kenawy; Abdelhameed Ibrahim. 2021. "Adaptive Dynamic Meta-heuristics for Feature Selection and Classification in Diagnostic Accuracy of Transformer Faults." IEEE Access 9, no. : 1-1.

Journal article
Published: 20 May 2021 in Energies
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The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature.

ACS Style

Youcef Benmahamed; Omar Kherif; Madjid Teguar; Ahmed Boubakeur; Sherif Ghoneim. Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier. Energies 2021, 14, 2970 .

AMA Style

Youcef Benmahamed, Omar Kherif, Madjid Teguar, Ahmed Boubakeur, Sherif Ghoneim. Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier. Energies. 2021; 14 (10):2970.

Chicago/Turabian Style

Youcef Benmahamed; Omar Kherif; Madjid Teguar; Ahmed Boubakeur; Sherif Ghoneim. 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier." Energies 14, no. 10: 2970.

Journal article
Published: 13 May 2021 in Electronics
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Radio frequency energy harvesting is one of the new renewable sources that faces some technical challenges, which limit its performance. This study presents two scenarios to enhance the harvested power. The first scenario introduces a quad-band voltage multiplier circuit with a single receiving antenna and four band-pass filters of elliptic type. In this scenario, four frequencies of the Global System for Mobile communications, Universal Mobile Telecommunications System, and Wireless Fidelity frequency bands have been considered for the study. The second scenario proposes a quad-band voltage multiplier circuit with four receiving antennas at the same frequency bands as the first scenario. High conversion efficiencies were achieved for the two scenarios. The proposed quad-band system developed a harvested power level, sufficient for powering up low power micro-devices with no need for an external power supply.

ACS Style

Kyrillos Selim; Shaochuan Wu; Demyana Saleeb; Sherif Ghoneim. A Quad-Band RF Circuit for Enhancement of Energy Harvesting. Electronics 2021, 10, 1160 .

AMA Style

Kyrillos Selim, Shaochuan Wu, Demyana Saleeb, Sherif Ghoneim. A Quad-Band RF Circuit for Enhancement of Energy Harvesting. Electronics. 2021; 10 (10):1160.

Chicago/Turabian Style

Kyrillos Selim; Shaochuan Wu; Demyana Saleeb; Sherif Ghoneim. 2021. "A Quad-Band RF Circuit for Enhancement of Energy Harvesting." Electronics 10, no. 10: 1160.

Journal article
Published: 09 April 2021 in Processes
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In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr ), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.

ACS Style

Sherif Ghoneim; Sobhy Dessouky; Ahmed Boubakeur; Adel Elfaraskoury; Ahmed Abou Sharaf; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects. Processes 2021, 9, 657 .

AMA Style

Sherif Ghoneim, Sobhy Dessouky, Ahmed Boubakeur, Adel Elfaraskoury, Ahmed Abou Sharaf, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects. Processes. 2021; 9 (4):657.

Chicago/Turabian Style

Sherif Ghoneim; Sobhy Dessouky; Ahmed Boubakeur; Adel Elfaraskoury; Ahmed Abou Sharaf; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects." Processes 9, no. 4: 657.

Journal article
Published: 10 March 2021 in IEEE Access
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Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids’ components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

ACS Style

Fengyuan Yin; Ali Hajjiah; Kittisak Jermsittiparsert; Ameena Saad Al-Sumaiti; Salah K. Elsayed; Sherif S. M. Ghoneim; Mohamed A. Mohamed. A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids. IEEE Access 2021, 9, 40797 -40810.

AMA Style

Fengyuan Yin, Ali Hajjiah, Kittisak Jermsittiparsert, Ameena Saad Al-Sumaiti, Salah K. Elsayed, Sherif S. M. Ghoneim, Mohamed A. Mohamed. A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids. IEEE Access. 2021; 9 ():40797-40810.

Chicago/Turabian Style

Fengyuan Yin; Ali Hajjiah; Kittisak Jermsittiparsert; Ameena Saad Al-Sumaiti; Salah K. Elsayed; Sherif S. M. Ghoneim; Mohamed A. Mohamed. 2021. "A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids." IEEE Access 9, no. : 40797-40810.

Journal article
Published: 28 February 2021 in Processes
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This paper presents the computation of the cable ampacity and the temperature distribution through long duration based on the equivalent thermal circuit based on IEC 60287 standard and the Finite element method using COMSOL (Multiphysics environment, version 5.5). This study investigated the cable ampacity and the temperature rise of the cable core and sheath at steady state and emergency conditions. The cable ampacity was investigated at different conditions such as the variation of cable depth, soil properties, and soil temperature. The results confirmed the adaptation between the thermal circuit results and the COMSOL results as well as the effectiveness of using the numerical method to compute the cable ampacity. Using the COMSOL-based thermal properties evaluations, the transient performance of the cable is ascertained. The transient study is performed for different cable loading currents and dry zone sizes.

ACS Style

Sherif Ghoneim; Mahrous Ahmed; Nehmdoh Sabiha. Transient Thermal Performance of Power Cable Ascertained Using Finite Element Analysis. Processes 2021, 9, 438 .

AMA Style

Sherif Ghoneim, Mahrous Ahmed, Nehmdoh Sabiha. Transient Thermal Performance of Power Cable Ascertained Using Finite Element Analysis. Processes. 2021; 9 (3):438.

Chicago/Turabian Style

Sherif Ghoneim; Mahrous Ahmed; Nehmdoh Sabiha. 2021. "Transient Thermal Performance of Power Cable Ascertained Using Finite Element Analysis." Processes 9, no. 3: 438.

Journal article
Published: 27 February 2021 in Processes
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The continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy.

ACS Style

Sherif Ghoneim. Determination of Transformers’ Insulating Paper State Based on Classification Techniques. Processes 2021, 9, 427 .

AMA Style

Sherif Ghoneim. Determination of Transformers’ Insulating Paper State Based on Classification Techniques. Processes. 2021; 9 (3):427.

Chicago/Turabian Style

Sherif Ghoneim. 2021. "Determination of Transformers’ Insulating Paper State Based on Classification Techniques." Processes 9, no. 3: 427.

Journal article
Published: 18 February 2021 in IEEE Access
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The early detection of the transformer faults with high accuracy rates guarantees the continuous operation of the power system networks. Dissolved gas analysis (DGA) is a technique that is used to detect or diagnose the transformer faults based on the dissolved gases due to the electrical and thermal stresses influencing the insulating oil. Many attempts are accomplished to discover an appropriate technique to correctly diagnose the transformer fault types, such as the Duval Triangle method, Rogers' ratios method, and IEC standard 60599. In addition, several artificial intelligence, classification, and optimization techniques are merged with the previous methods to enhance their diagnostic accuracy. In this article, a novel approach is proposed to enhance the diagnostic accuracy of the transformer faults based on introducing new gas concentration percentages limits and gases' ratios that help to separate the conflict between the diverse transformer faults. To do so, an optimization model is established which simultaneously optimizes both gas concentration percentages and ratios so as to maximize the agreement of the diagnostic faults with respect to the actual ones achieving the high diagnostic accuracy of the transformer faults. Accordingly, an efficient teaching-learning based optimization (TLBO) is developed to accurately solve the optimization model considering training datasets (Egyptian chemical laboratory and literature). The proposed TLBO algorithm enhances diagnostic accuracy at a significant level, which is higher than some of the other DGA techniques that were presented in the literature. The robustness of the proposed optimization-based approach is confirmed against uncertainty in measurement where its accuracy is not affected by the uncertainty rates. To prove the efficacy of the proposed approach, it is compared with five existing approaches using an out-of-sample dataset where a superior agreement rate is reached for the different fault types.

ACS Style

Sherif S. M. Ghoneim; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. Enhancing Diagnostic Accuracy of Transformer Faults Using Teaching-Learning-Based Optimization. IEEE Access 2021, 9, 30817 -30832.

AMA Style

Sherif S. M. Ghoneim, Karar Mahmoud, Matti Lehtonen, Mohamed M. F. Darwish. Enhancing Diagnostic Accuracy of Transformer Faults Using Teaching-Learning-Based Optimization. IEEE Access. 2021; 9 ():30817-30832.

Chicago/Turabian Style

Sherif S. M. Ghoneim; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Enhancing Diagnostic Accuracy of Transformer Faults Using Teaching-Learning-Based Optimization." IEEE Access 9, no. : 30817-30832.

Journal article
Published: 09 February 2021 in IEEE Access
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Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources’ ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

ACS Style

Mohamed I. Mosaad; Haitham Saad Mohamed Ramadan; Mansour Aljohani; Mohamed F. El-Naggar; Sherif S. M. Ghoneim. Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System. IEEE Access 2021, 9, 34119 -34130.

AMA Style

Mohamed I. Mosaad, Haitham Saad Mohamed Ramadan, Mansour Aljohani, Mohamed F. El-Naggar, Sherif S. M. Ghoneim. Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System. IEEE Access. 2021; 9 (99):34119-34130.

Chicago/Turabian Style

Mohamed I. Mosaad; Haitham Saad Mohamed Ramadan; Mansour Aljohani; Mohamed F. El-Naggar; Sherif S. M. Ghoneim. 2021. "Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System." IEEE Access 9, no. 99: 34119-34130.

Journal article
Published: 28 January 2021 in Energies
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The aging of power transformers causes several defects and damages in the insulating system, especially in the insulating paper. The degradation of the insulating paper generates dissolved gases in the insulating oil, which are measured by gas chromatography and used as an indicator of the insulation status. The state of the insulating paper can be identified based on the degree of polymerization (DP) measurement. In some cases, when the measurement of DP is difficult, estimating DP can be accomplished through gathering information about some of the testing parameters, such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldhyde (FA)). The statistical tools (correlation and multiple linear regression), based on 131 transformer samples, can be used to build a relation linking DP and one or more of the previous parameters to identify the insulating paper status and the percentage of remaining life of the transformer. The results indicated that it is difficult to build a mathematical model to relate between the DP and the testing variables, except with FA, where the trend of DP with FA is more obvious than with other variables. The empirical formula to compute DP based on the FA concentration was developed and gave promising results to compute DP and the remaining life of the power transformers.

ACS Style

Sherif S. M. Ghoneim. The Degree of Polymerization in a Prediction Model of Insulating Paper and the Remaining Life of Power Transformers. Energies 2021, 14, 670 .

AMA Style

Sherif S. M. Ghoneim. The Degree of Polymerization in a Prediction Model of Insulating Paper and the Remaining Life of Power Transformers. Energies. 2021; 14 (3):670.

Chicago/Turabian Style

Sherif S. M. Ghoneim. 2021. "The Degree of Polymerization in a Prediction Model of Insulating Paper and the Remaining Life of Power Transformers." Energies 14, no. 3: 670.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Sherif S. M. Ghoneim; Amr E. Rashed; Nagy I. Elkalashy. Fault Detection Algorithms for Achieving Service Continuity in Photovoltaic Farms. Intelligent Automation & Soft Computing 2021, 29, 467 -479.

AMA Style

Sherif S. M. Ghoneim, Amr E. Rashed, Nagy I. Elkalashy. Fault Detection Algorithms for Achieving Service Continuity in Photovoltaic Farms. Intelligent Automation & Soft Computing. 2021; 29 (3):467-479.

Chicago/Turabian Style

Sherif S. M. Ghoneim; Amr E. Rashed; Nagy I. Elkalashy. 2021. "Fault Detection Algorithms for Achieving Service Continuity in Photovoltaic Farms." Intelligent Automation & Soft Computing 29, no. 3: 467-479.