In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informe...In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system management.It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so on.Various methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration.This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)algorithm.The objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing processes.In this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in manufacturing.Random Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing performance.Additionally,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method further.The method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and specificity.Results show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.展开更多
In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for n...In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.展开更多
基金the Deputyship for Research and Innovation,Ministry of Education,Saudi Arabia,for funding this research(IFKSUOR3-176-8).
文摘In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system management.It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so on.Various methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration.This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)algorithm.The objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing processes.In this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in manufacturing.Random Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing performance.Additionally,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method further.The method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and specificity.Results show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.
基金supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University in Saudi Arabia under Project Number(ICR-2024-1002).
文摘In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.
