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Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

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International Journal of Minerals,Metallurgy and Materials 2024年第7期31卷 1500-1511页

作者： Dapeng Chen Shenghua Yin Weiguo Long Rongfu Yan Yufei Zhang Zepeng Yan Leiming Wang Wei Chen School of Civil and Resources Engineering University of Science and Technology of BeijingBeijing 100083China Production Technology Department Longshou MineJinchuan Group Co.Ltd.Jinchang 737100China School of Civil Engineering and Architecture East China Jiaotong UniversityNanchang 330013China

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress *** monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring *** solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock *** three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and *** information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously *** with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then *** this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock *** evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock *** new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou *** moments of mutation in these time series were at least 3 months ahead of the roadway return *** the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0

关键词： deep mining filling body–surrounding rock combination phase space reconstruction multiple time series stability prediction

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Water Wave Optimization with Deep Learning Driven Smart Grid stability prediction

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Computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid stability prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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Metaheuristic Optimization with Deep Learning Enabled Smart Grid stability prediction

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Computers, Materials & Continua 2023年第6期75卷 6395-6408页

作者： Afrah Al-Bossly Department of Mathematics College of Science and Humanities in Al-KharjPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Due to the drastic increase in global population as well as economy,electricity demand becomes considerably *** recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power *** learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability *** article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid stability(SSODLSA-SGS)pre-diction ***,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)*** SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and ***,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG ***,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN *** design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS *** extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

关键词： Smart grids stability prediction deep learning statistical analysis social spider optimization

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Intelligent Smart Grid stability Predictive Model for Cyber-Physical Energy Systems

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Computer Systems Science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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