Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ...Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.展开更多
The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying...The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.展开更多
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.
基金the project“Research on Power SafetyDecision Support SystemBased on Large Language Models”(Science and Technology Project of Huaian Hongneng Group Co.,Ltd.)under Contract No.SGTYHT/23-JS-001.
文摘The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems.Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multimodal data fusion.This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large languagemodels(LLMs)with adaptive optimization,achieving three key innovations:(1)Ahierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction,(2)Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds(Daubechies-4 basis,6-level decomposition),and(3)A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating.Validated on IEEE 33/100-node systems,our framework demonstrates 96.7%fault localization accuracy(23%improvement over STGCN)and 0.82-s protection delay,outperforming MILP-basedmethods by 37%in reconfiguration speed.The system maintains 98.4%self-healing success rate under cascading faults,resolving 89.3%of phase-toground faults within 500 ms through adaptive impedance matching.Field tests on 220 kV substations with 45%renewable penetration show 99.1%voltage stability(±5%deviation threshold)and 40%communication efficiency gains via compressed GOOSE message parsing.Comparative analysis reveals 12.6×faster convergence than conventional ACO in 1000-node networks,with 95.2%robustness against±25%load fluctuations.These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids,reducing outage duration by 63%inmulti-agent simulations compared to centralized architectures.
