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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

Integration of automatic voltage regulator and power system stabilizer: small-signal stability in DFIG-based wind farms

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Journal of Modern Power Systems and Clean Energy 2019年第5期7卷 1115-1128页

作者： Sirine ESSALLAH adel BOUALLEGUE adel KHEDHER Universite de Sousse Ecole Nationale d’Ingenieurs de SousseLATIS-Laboratory of Advanced Technology and Intelligent Systems4023 SousseTunisia

The increasing penetration of wind farms in the energy sector directly affects the dynamic behavior of the power *** increasing use of wind energy in the power system worsens its stability and inherently influences the firmness of a small *** investigate these effects,one of the synchronous generators(SGs)of the grid is considered defective and is replaced by a doubly fed induction generator(DFIG)-based wind farm of the same *** small-signal stability of a power system is usually evaluated via eigenvalue analysis where local-area and inter-area oscillatory modes for the New England test system are *** controls,such as automatic voltage regulator(AVR)and power system stabilizer(PSS),are added to attenuate the generated *** this study,the impact of wind energy on the small-signal stability of the power system is *** combinations of AVR and PSS types are considered to mitigate the undesirable alterations.A comparative study is performed based on numerical simulations to choose the best combination of AVR and PSS *** obtained results prove that the proposed combination yields good results in terms of stability enhancement both under normal operating conditions and in DFIG-based wind farms.

关键词： Power system stability Power transmission system Transient analysis Voltage regulation

Enhancing Thermal Performance of a Parabolic Trough Collector with Inserting Longitudinal Fins in the Down Half of the Receiver Tube

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Journal of Thermal Science 2020年第5期29卷 1309-1321页

作者： LAARABA adel MEBARKI Ghazali Department of physics University of BatnalBatna 05000Algeria Faculty of Technology LESEI LaboratoryDepartment of Mechanical EngineeringUniversity of Batna2Batna 05000Algeria

Heat transfer in a finned absorber of a parabolic trough collector was studied numerically. The main aim of this work was to study the effect of attached fins on the enhancement of the thermal performance of a parabolic trough collector. The values of the fin's length varied from 0 to 20 mm;their thicknesses varied from 0 to 8 mm and their number was 5. The parameters used in the current study are: the thermal and dynamic field,friction coefficient, Nusselt number, the thermal efficiency and thermal enhancement index. Obtained results show that inclusion of fins to the lower half of the absorber tube can enhance the heat transfer between the absorber tube and working fluid. The increase of the fin*s length increases the friction factor, Nusselt number and thermal efficiency, and the increase of fin's thickness also increases the previous parameters. Starting the value 6 mm of thickness, its effect remains the same, but thickness is less effective than length. The values 15 mm of length and 6 mm of thickness are selected as optimal values. Results show that the inclusion of the fins enhances the thermal performance of the parabolic collector by 8.45%.

关键词： solar energy Nusselt number thermal efficiency parabolic trough collector forced convection

Mesoporous WO_3-graphene photocatalyst for photocatalytic degradation of Methylene Blue dye under visible light illumination

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Journal of Environmental Sciences 2018年第4期30卷 328-337页

作者： adel A.Ismail M.Faisal adel Al-Haddad Wastewater Treatment and Reclamation Technologies (WTRT) Water Research Center Kuwait Institute for Scientific Research Advanced Materials and Nano-Research Centre Najran University

Advanced oxidation technologies are a friendly environmental approach for the remediation of industrial wastewaters. Here, one pot synthesis of mesoporous WO3 and WO3-graphene oxide（GO） nanocomposites has been performed through the sol–gel method. Then, platinum（Pt） nanoparticles were deposited onto the WO3 and WO3-GO nanocomposite through photochemical reduction to produce mesoporous Pt/WO3 and Pt/WO3-GO nanocomposites. X-ray diffraction（XRD） findings exhibit a formation of monoclinic and triclinic WO3 phases. Transmission Electron Microscope（TEM） images of Pt/WO3-GO nanocomposites exhibited that WO3 nanoparticles are obviously agglomerated and the particle sizes of Pt and WO3 are ~ 10 nm and 20–50 nm, respectively. The mesoporous Pt/WO3 and Pt/WO3-GO nanocomposites were assessed for photocatalytic degradation of Methylene Blue（MB） as a probe molecule under visible light *** findings showed that mesoporous Pt/WO3, WO3-GO and Pt/WO3-GO nanocomposites exhibited much higher photocatalytic efficiencies than the pure WO3. The photodegradation rates by mesoporous Pt/WO3-GO nanocomposites are 3, 2 and 1.15 times greater than those by mesoporous WO3, WO3-GO, and Pt/WO3, respectively. The key factors of the enhanced photocatalytic performance of Pt/WO3-GO nanocomposites could be explained by the highly freedom electron transfer through the synergetic effect between WO3 and GO sheets, in addition to the Pt nanoparticles that act as active sites for O2 reduction, which suppresses the electron hole pair recombination in the Pt/WO3-GO nanocomposites.

关键词： Mesoporous Pt/WO3 nanocomposites Graphene oxide Methylene Blue photodegradation Visible light

Anomaly Classification Using Genetic Algorithm-Based Random Forest Modelfor Network Attack Detection

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Computers, Materials & Continua 2021年第1期66卷 767-778页

作者： adel Assiri Management Information Systems Department College of BusinessKing Khalid UniversityAbha61421Saudi Arabia

Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion ***-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen *** several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection ***,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification ***,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection *** this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network *** validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation *** these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent *** results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time.

关键词： Network-based intrusion detection system(NIDS) random forest classifier genetic algorithm KDD99 UNSW-NB15

Characterization of component interactions in two-stage axial turbine

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Chinese Journal of Aeronautics 2016年第4期29卷 893-913页

作者： adel Ghenaiet Kaddour Touil Laboratory of Energetics and Conversion Systems Faculty of Mechanical Engineering University of Sciences and Technology Houari Boumediene Laboratory of Thermal Power Systems Applied Mechanics

This study concerns the characterization of both the steady and unsteady flows and the analysis of stator/rotor interactions of a two-stage axial turbine. The predicted aerodynamic performances show noticeable differences when simulating the turbine stages simultaneously or separately. By considering the multi-blade per row and the scaling technique, the Computational fluid dynamics(CFD) produced better results concerning the effect of pitchwise positions between vanes and blades. The recorded pressure fluctuations exhibit a high unsteadiness characterized by a space–time periodicity described by a double Fourier decomposition. The Fast Fourier Transform FFT analysis of the static pressure fluctuations recorded at different interfaces reveals the existence of principal harmonics and their multiples, and each lobed structure of pressure wave corresponds to the number of vane/blade count. The potential effect is seen to propagate both upstream and downstream of each blade row and becomes accentuated at low mass flow rates. Between vanes and blades, the potential effect is seen to dominate the quasi totality of blade span, while downstream the blades this effect seems to dominate from hub to mid span. Near the shroud the prevailing effect is rather linked to the blade tip flow structure.

关键词： CFD FFT analysis Pressure fluctuations Two-stage axial turbine Unsteady flows Vane/blade interactions

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

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Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin nikanjam adel Rahmani School of Computer Engineering Iran University of Science and Technology

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation Bayesian optimization algorithm Bayesian network model building bivariate interaction

Efficient Training of Multi-Layer Neural Networks to Achieve Faster Validation

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Computer Systems Science & Engineering 2021年第3期36卷 435-450页

作者： adel Saad Assiri Management Information Systems Department College of BusinessKing Khalid UniversityAbhaSaudi Arabia

Artificial neural networks(ANNs)are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines,including but not limited to physics,biology,chemistry,and ***,ANNs lack several key characteristics of biological neural networks,such as sparsity,scale-freeness,and *** concept of sparse and scale-free neural networks has been introduced to fill this *** sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random *** the network is initialized,the neural network is fully connected,which means the number of weights is four times the number of *** this study,considering that a biological neural network has some degree of initial sparsity,we design an ANN with a prescribed level of initial *** neural network is tested on handwritten digits,Arabic characters,CIFAR-10,and Reuters newswire *** show that it is possible to reduce the number of weights by up to 50%without losing prediction ***,in both cases,the testing time is dramatically reduced compared with fully connected ANNs.

关键词： Sparsity weak weights multi-layer neural network NN training with initial sparsity

Inverse full state hybrid projective synchronization for chaotic maps with different dimensions

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Chinese Physics B 2016年第9期25卷 251-256页

作者： adel Ouannas Giuseppe Grassi Laboratory of Mathematics Informatics and Systems (LAMIS) University of Larbi Tebessi Tebessa 12002 Algeria University del Salento Dipartimento Ingegneria Innovazione 73100 Lecce Italy

A new synchronization scheme for chaotic（hyperchaotic） maps with different dimensions is ***,given a drive system map with dimension n and a response system with dimension m,the proposed approach enables each drive system state to be synchronized with a linear response combination of the response system *** method,based on the Lyapunov stability theory and the pole placement technique,presents some useful features：（i） it enables synchronization to be achieved for both cases of n 〈 m and n 〉 m;（ii） it is rigorous,being based on theorems;（iii） it can be readily applied to any chaotic（hyperchaotic） maps defined to ***,the capability of the approach is illustrated by synchronization examples between the two-dimensional H′enon map（as the drive system） and the three-dimensional hyperchaotic Wang map（as the response system）,and the three-dimensional H′enon-like map（as the drive system） and the two-dimensional Lorenz discrete-time system（as the response system）.

关键词： chaotic map full state hybrid projective synchronization inverse problem maps with different dimensions