Ragab A. El-Sehiemy
6504618921
Publications - 11
Generic optimal power flow solution associated with technical improvements and emission reduction by multi-objective ARO algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
In modern power engineering, the optimal operation aims to achieve the basic requirements of the electrical power grid, meet various technical and economic aspects, and preserve the environmental limits within their accepted bounds. In this line, the current paper finds the optimal operational scheduling of the power generation units that cover the load requirements, considering different frameworks of the optimal power flow (OPF) problem involving single- and multi-objective functions. Technical, economic, and emissions objective functions are considered. Artificial rabbits’ optimization (ARO) is developed to find the optimal OPF framework solution. The effectiveness of the proposed algorithm is evaluated through a comprehensive comparison study with the existing works in the literature. With six IEEE standard power systems, 22 different cases are implemented to test the ARO performance as an alternative to solve the OPF problem. Two of these systems are considered small-size systems, 30-, and 57-test systems, while the other four are large-scale power systems (IEEE 300, 1354, 3012, and 9241 test systems) to expand the validation scope of this paper. This comparison validates the scalability and efficiency of the ARO algorithm. The impact of varied population size and maximum iteration number is tested for two test systems, the most benchmarking test systems. It was proven that the routine of ARO has robust and superior competitive performance compared with others at fine convergence rates. Significant improvements are acquired in the range of 47% in the technical and economic issues by accepting the environmental concerns.
Open Access: Yes
Generic multidimensional economic environmental operation of power systems using equilibrium optimization algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The economic emission load dispatch (EELD) problem is one of the main challenges to power system operators due to the complexity of the interconnected power systems and the non-linear characteristics of the objective functions (OFs). Therefore, the EELD problem has attracted significant attention in the electric power system because it has important objectives. Thus, this paper proposes the equilibrium optimization algorithm (EOA) to solve the EELD problem in electrical power systems by minimizing the total fuel cost and emissions, considering system and operational constraints. The OFs are optimized with and without considering valve point effects (VPE) and transmission system loss. The multi-OF, which aims to optimize these objectives simultaneously, is considered. In the proposed EOA, agents are particles and concentrations that express the solution and position, respectively. The proposed EOA is evaluated and tested on different-sized standard test systems having 10, 20, 40, and 80 generation units through several case studies. The numerical results obtained by the proposed EOA are compared with other optimization techniques such as grey wolf optimization, particle swarm optimization (PSO), differential evolution algorithm, and other optimization techniques in the literature. To show the reliability of the proposed algorithm for solving the considered OFs on a large-scale power system with and without considering different practical constraints such as VPE, ramp-rate limits (RRL), and prohibited operating zones (POZs) of generating units, the proposed EOA is evaluated and tested on the 140-unit test system. Also, the proposed multi-objective EOA (MOEOA) successfully acquires the Pareto optimal front to find the best compromise solution between the considered OFs. Also, the statistical analysis and the Wilcoxon signed rank test between the EOA and other optimization techniques for solving the EELD problem are performed. From numerical results, the total fuel cost obtained without considering VPE using the proposed EOA is reduced by 0.1414%, 0.1295%, 0.6864%, 5.8441% than the results of PSO, with maximum savings of 150 $/hr, 78 $/hr, 820 $/hr, and 14,730 $/hr for 10, 20, 40, and 80 units, respectively. The total fuel cost considering VPE is reduced by 0.0753%, 0.2536%, 2.8891%, and 3.6186% than the base case with maximum savings of 80 $/hr, 158 $/hr, 3610 $/hr, 9230 $/hr for 10, 20, 40, and 80 units, respectively. The total emission is reduced by 1.7483%, 12.8673%, and 7.5948% from the base case for 10, 40, and 80 units, respectively. For the 140-unit test system, the total fuel cost without and with considering VPE, RRL, and POZs is reduced by 6.4203% and 7.2394%, than the results of PSO with maximum savings of 107,200 $/hr and 126,400 $/hr. The total emission is reduced by 2.5688% from the base case. The comparative studies show the superiority of the EOA for the economic/environmental operation of the power system by solving the EELD problem with more accuracy and efficiency, especially as the system size increases.
Open Access: Yes
High precision experimentally validated adaptive neuro fuzzy inference system controller for DC motor drive system
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The control of a dc motor drive system is performed using two Proportional-Integral (PI) controllers, mostly comprising the outer (speed) and inner (torque) loops. However, the tuning of the PI controllers is challenging, because of the significant overshoot and substantial settling time. The overshoot cannot be absolutely removed without sacrificing the speediness. In this paper, an experimental implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) based high-precision controllers for a dc motor drive system is presented for eliminating the overshoot contemporaneously improving the settling time. In this regard, for fair comparison, Bode Plot method is used for obtaining most satisfactory values of Kp and Ki of both the controllers and the performance of the proposed ANFIS controllers is compared with the tuned PI controllers. For the sake of validation, an experimental setup, a 2-quadrant dc motor drive based on DS1104 R&D controller board from dSPACE is used. The designed ANFIS controllers are implemented and also authenticated using simulations. The obtained simulation as well as experimental results indicated that the ANFIS controller successfully eliminates the overshoot and significantly improves settling time. Thereby, outclass the tuned PI controllers by giving experimental results as 0% overshoot and just 0.18 s settling time.
Open Access: Yes
Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Energy hubs (EHs) are considered a promising solution for multi-energy resources, providing advanced system efficiency and resilience. However, their operation is often challenged by the need for techno-economic trade-offs and the uncertainties related to supply and demand. This research presents a multi-objective optimizing framework for EH operations tackling these techno-economic aspects under uncertainty. Utilizing artificial neural networks (ANN)-based active learning (AL), the proposed approach dynamically enhances the model’s capability to achieve optimal scheduling and planning while considering complex, fluctuating energy demands and system constraints. The optimization approach under uncertainty provides robust predictive abilities across various scenarios, allowing the system to optimize energy management effectively, enhancing operational efficiency while minimizing overall energy losses, costs, and emissions. Results demonstrate significant improvements in system reliability, cost efficiency, and flexible operation, validating the effectiveness of ANN-based AL to optimize EHs management and ensure sustainable operation complexities. The AL algorithm enhances the ANN model’s predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. It ensures energy efficiency while sustaining system flexibility, adapting to frequent load dynamics and intermittent renewable energy supply. The algorithm minimizes electrical and thermal deviations, achieving a balance of flexible operation with efficient energy management. Despite uncertainties and intermittent renewable energy supply, the AL optimizes renewables utilization and demand adjustments, reducing energy losses, costs, and emissions by 80.3The optimized system achieves an output of 13,687.8 kW per day. The system’s implementation is performed using MATLAB R2023b software, ensuring precision and efficiency.
Open Access: Yes
A proposed wavelet analysis based fault diagnosis scheme of power transformers using fault signatures and CT saturation
Publication Name: Results in Engineering
Publication Date: 2025-09-01
Volume: 27
Issue: Unknown
Page Range: Unknown
Description:
Diagnosis of concealed internal faults within power transformer is a key for high grid reliability to ensure continuity of power supply to customers. One of the urgent situations of power transformer is the faults under CT saturation and the operation under inrush currents that lead to huge failure of fault identification of the power transformer. In this paper, a fault identification scheme is designed using details and approximate coefficients obtained by discreet wavelet transform applied to a differential current signal under different situations. Also, this paper considers the impact of transformer internal faults such as turn to earth and turn to turn faults, external faults, and inrush currents. The signature of processing differential current is employed for identifying these fault conditions since such fault has a distinct differential current signature. The simulation tests are performed on a 115/22 kV power transformer using ATP-EMTP real-time simulator. Different wavelet families are assessed to show that the optimum mother wavelet, db1, has high fault detection and classification performance. The proposed scheme is verified for transformer energization conditions, and the influence of CT saturation is also considered in this study. Moreover, one of the most important proposed scheme features is simplicity with high lights aspects toward all fault conditions and fault types at different fault location and different fault resistances. Intensive simulation results are obtained to prove the improved selectivity and sensitivity of the proposed scheme for identifying internal transformer faults. Furthermore, sensitivity analysis is not only conducted in terms of transformer loading and fault resistance variation, but transformer scalability study is also verified. Finally, to evaluate the performance of the proposed scheme, an assessment study is adopted to show the accuracy and reliability of differential protection scheme.
Open Access: Yes
Accurate multi-phase unsupervised and supervised approach to fault detection in power transmission networks
Publication Name: Neural Computing and Applications
Publication Date: 2025-08-01
Volume: 37
Issue: 24
Page Range: 19751-19772
Description:
Transmission line faults can cause both power loss and failure. To mitigate the effects of such faults, the energy supply must quickly identify and remove faults, as well as ensure grid restoration after a failure occurs. As a result, it is critical to design a system capable of quickly and reliably identifying and eliminating errors. The level of fault identification accuracy is an important indicator for ensuring the reliability of main equipment in power systems, such as generators and transformers. This paper proposes a two-phase approach for identifying faults in transmission systems. In the first phase, unsupervised learning techniques like K-Mean clustering are used to assign labels to datasets for transmission line fault classification. During the second phase, four machine learning techniques called logistic regression (LR), decision tree classifier (DTC), random forest classifier (RFC), and XGBoost Classifier (XGB) are employed to identify faults. Applications are validated on fault detection datasets. The tested approach provides an efficient model for fault detection and classification in transmission lines, as well as a productive framework for fault detection prediction based on machine learning and ensemble learning methods. The experimental simulation results from this study show an accuracy of 83.6% for LR, 99% for DTC, 99% for RFC, and 99.9% for XGB, LightGBM, and CatBoost at 0.99995%. The paper's findings demonstrate the effectiveness of machine learning and ensemble learning techniques in accurately identifying and classifying transmission line faults at competitive performance indices.
Open Access: Yes
Optimal parameter extraction of equivalent circuits for single- and three- phase Power transformers based on arctic puffin algorithm accomplished with experimental verification
Publication Name: Results in Engineering
Publication Date: 2025-06-01
Volume: 26
Issue: Unknown
Page Range: Unknown
Description:
The power transformer is a critical device in power systems. This paper addresses one of the major problems which hopes to enhance the accuracy of estimation of parameters, which is critical in power transformer modeling, maintenance, and operating efficiency. In that context, this work estimates the parameters of single- and three-phase power transformers by a new optimizer called Arctic Puffin Optimization Algorithm (APO). The algorithm is intended to improve estimation of transformer parameters with the goal of reducing the error incurred between the estimated and actual values of the parameters. To verify the accuracy of the APO, experimental measurements were conducted on single- and three-phase transformers. The assessment of the algorithm's effectiveness was performed against the effectiveness of other commonly used estimating methods. The results have shown that APO increases the accuracy of estimation of the parameters of both single- and three-phase transformers to considerable levels. Dependability of the APO was established by experimental verification, which disclosed an ultimate connection between the resultant quantities and actual measurements. The study also confirmed APO can be useful for transformer parameter estimation because APO converges more rapidly and more precisely compared with traditional methods of the literature.
Open Access: Yes
Adaptive Speed Tuning of Permanent Magnet Synchronous Motors Using Intelligent Fuzzy Based Controllers for Pumping Applications
Publication Name: Processes
Publication Date: 2025-05-01
Volume: 13
Issue: 5
Page Range: Unknown
Description:
This study focuses on enhancing the performance of Permanent Magnet Synchronous Motors (PMSMs) in pumping applications by improving motor torque through the integration of advanced control strategies. The dq-axis model of a PMSM is utilized to facilitate precise control and dynamic response. The proposed approach combines Fuzzy Logic Control (FLC) and Fuzzy Proportional-Integral-Derivative (fuzzy PID) controllers with Vector Control (VC) inverters, specifically designed for PMSMs with salient rotor structures. The salient rotor design inherently provides higher torque density, making it suitable for demanding applications like pumping. The FLC and fuzzy PID controllers are employed to optimize the motor’s dynamic response, ensuring precise torque control and improved efficiency under varying load conditions. The VC inverter further enhances the system’s performance by enabling rapid torque and flux control, reducing torque ripple, and improving overall motor stability. The simulation results demonstrate that the proposed control strategy significantly increases motor torque, enhances energy efficiency, and reduces operational losses in pumping applications. This makes the system more reliable and cost-effective for industrial and agricultural pumping systems, where high torque and energy savings are critical. The integration of FLC, fuzzy PID, and VC with a salient-rotor PMSM offers a robust solution for achieving superior motor performance in real-world pumping scenarios. This work contributes to the development of smarter, more efficient pumping systems, paving the way for enhanced industrial automation and energy management.
Open Access: Yes
DOI: 10.3390/pr13051393
Detection of Harmonic and Interharmonics Contents in Water Desalination Plants' Distribution System Based on Deep Learning Algorithms
Publication Name: Energy Science and Engineering
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Water desalination plants are significant consumers of electric power, making them some of the largest energy users in power grids. Their electricity consumption presents an urgent challenge for efficient and sustainable operation, and they are among the most power-quality-threatening customers for utilities. This study presents three distinct strategies to enhance prediction accuracy and extend forecasting horizons, aiming to reduce algorithmic and hardware delays. Additionally, it suggests effective methods to compensate for voltage fluctuations, voltage flicker, and dips arising from desalination plants. The paper also discusses forecasting harmonics and interharmonics in current signals. Furthermore, it integrates the above techniques into a comprehensive computing system, along with an active power filter (APF) scheme, within the Simulink framework. A comparison is drawn between the performance of predictive techniques in an APF and a conventional, non-predictive APF. The proposed data augmentation method successfully increases prediction accuracy. By effectively forecasting upcoming waveforms, it reduces algorithmic and hardware delays. These techniques are designed to address multiple power quality issues simultaneously, including harmonics, interharmonics, flicker, and voltage dips, which often coexist in the spectrum as interharmonics. The suggested approach employs Long Short-Term Memory (LSTM) networks combined with the Jetson TX2 embedded artificial intelligence computer to accelerate machine learning applications. This method has proven effective in predicting and classifying time series data, including harmonics, interharmonics, and raw current signals, achieving 100% accuracy. This eliminates the need for designing specific low-pass filters. The evaluation results for this time-domain deep learning-based technique will be reported in the subsections below. The implementation is conducted in Python using the KERAS deep learning framework and TensorFlow backend, and it is evaluated on a workstation equipped with an Intel i7 processor running at 4.0 GHz and 48 GB of RAM.
Open Access: Yes
DOI: 10.1002/ese3.70251
Optimal harmonics prediction for distribution systems powered by multi-energy sources using bidirectional long-short term memory combined with data sequence
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 184
Issue: Unknown
Page Range: Unknown
Description:
A multi-energy resource aims to maintain a balance between energy output and load consumption and to ensure power continuity during different operating conditions. The harmonic distortions can be estimated from the output current of a harmonic source, which may not fully reflect its true harmonic distortions due to the interactions between the state changes at the power network level and the harmonic sources. System operators monitor each system's harmonic performance under different conditions of operation to find the actual contribution of grid-connected systems to harmonic-related issues. Development of machine learning algorithms leads to effective progress in the harmonic prediction and computation. In this paper, the combined data sequencing, and Bidirectional Long-Short Term Memory (Bi-LSTM) network has been exploited for the real-time harmonic prediction of future events in multi-energy sources. The validity of the proposed Model including the applications of ANFIS, ANNs, MLRA and LSTM is conducted on the two standard systems as IEEE 9-bus and IEEE 34-bus multi energy resources system that is associated with PV systems. The simulation results, based on climate changes of solar irradiance and ambient temperature in PV systems, demonstrate that the proposed methods can accurately forecast changes in total harmonic distortion (THD) as well as the voltage profile at the point of common coupling. The performance of Bi-LSTM, original LSTM, Machine Linear Regression (MLR), and Artificial Neural Networks (ANNs) techniques were assessed. These findings provide valuable insights. Four performance validation indices, RMSE, R-squared and MSE are considered to assess the performance of the competitive learning algorithms. The results showed that in the model IEEE 9-bus, Bi-LSTM outperformed all the applied methods as its RMSE value was 0.000019 while its MSE value was 3.61e-10 and finally, the Bi-LSTM had a higher value squared error (R2) was equal 1 which indicates the effectiveness of Bi-LSTM for predicting sequential total harmonic distortion. On the other hand, in case study of IEEE 34-bus, the RMSE, MSE and R2 are 0, 3.276e-30 and 1 using Bi-LSTM which means that the Bi-LSTM leads to the best performance validation indices compared to other competitive algorithms for the tested multi-energy systems.
Open Access: Yes
Data-driven modelling of thermal conductivity in electrically aligned PDMS–diamond composites with experimental verification
Publication Name: Applied Thermal Engineering
Publication Date: 2025-12-01
Volume: 280
Issue: Unknown
Page Range: Unknown
Description:
Polymer-based composite material optimization is a key technology for achieving the desired thermal management in heat conduction sheets used in electronics and aerospace. Diamond particles are widely used as thermally conductive fillers in a liquid of poly di-methyl siloxane (PDMS) matrix because of their unique thermophysical properties. Electrical alignment is a powerful approach for filler alignment to achieve higher thermal conductivity. Meanwhile, practical experiments require substantial time, resources and consumable energy due to extensive testing. Therefore, it is essential to develop a highly robust predictive model for estimating thermal conductivity. This paper proposes a data-driven-based model that investigates a novel decision tree (DT) regression model for predicting thermal conductivity based on electrical alignment parameters, aiming to identify the optimal experimental conditions that achieve higher thermal conductivity. In this study, electrical alignment parameters, namely voltage, frequency, and rotational speed, are selected as descriptors for modelling and computing thermal conductivity. Correlation and multicollinearity analyses are conducted to evaluate the relationships among these descriptors. Three machine learning approaches, including Decision Tree, Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), are investigated alongside six empirical regression models. The predictive model-based refined DT achieves high accuracy with the lowest mean square error of 0.0004 and a higher coefficient of determination (R-squared) of 0. 9751on testing data, respectively. This indicates that the model is capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations with high closeness to the experimental records. This predictive model condition highlights the potential of DT-based method to precisely compute the thermal conductivity of PDMS-diamond composite based on the applied electrical alignment parameters.
Open Access: Yes
