DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown ...DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms.展开更多
The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information b...The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.展开更多
Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each sub...Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.展开更多
One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, ...One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.展开更多
In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identif...In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.展开更多
随着海上风力发电和光伏发电的快速发展,海洋输电工程的地位越来越重要,海底电缆的应用也越来越广泛.获得精确的海底电缆位置不仅有利于日常巡检,而且提高了故障检测的效率,因此,海底电缆的路由定位和故障检测将会是未来维护和维修的重...随着海上风力发电和光伏发电的快速发展,海洋输电工程的地位越来越重要,海底电缆的应用也越来越广泛.获得精确的海底电缆位置不仅有利于日常巡检,而且提高了故障检测的效率,因此,海底电缆的路由定位和故障检测将会是未来维护和维修的重要环节.由于海底电缆的小直径和内部电流的变化性,导致定位准确度的下降以及定位难度的上升.针对上述问题,首先,基于海底环境和水下机器人,利用三芯铠装海底电缆的电缆结构推导海底电缆外磁场的近似方程;然后,水下机器人根据检测到的磁感应强度值进行姿态调整,在此基础上,提出一种基于改进灰狼优化算法(improved grey wolf optimization,IGWO)的海底电缆定位算法,利用基于磁通密度模的适应度函数,设计一种用于海底电缆探测的在线路径定位方法;最后,通过仿真实验验证了IGWO算法实现海底电缆定位的精确性和有效性.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
A dynamic reconfiguration method for photovoltaic(PV)arrays based on an improved dung beetle algorithm(IDBO)to address the issue of PV array mismatch loss caused by partial shading conditions(PSCs)is proposed.To estab...A dynamic reconfiguration method for photovoltaic(PV)arrays based on an improved dung beetle algorithm(IDBO)to address the issue of PV array mismatch loss caused by partial shading conditions(PSCs)is proposed.To establish the output power-current(P-I)segmentation function for the total-cross-tied(TCT)PV array and the constraint function for the electrical switches,the IDBO algorithm was used to optimize both the P-I segmentation function and the electrical switch constraint function.IDBO is compared with algorithm-free reconfiguration and five other heuristic algorithms using two evaluation criteria:mismatch loss and power enhancement percentage,across six shading scenarios for 6x6 PV arrays.The irradiation distribution of PV arrays reconfigured by IDBO is also presented.The results show that IDBO effectively increases the output power of PV arrays and reduces mismatch loss.The output PV curves tend to exhibit a single peak,and the reconstruction results are superior to those obtained with the other methods.展开更多
基金supported by Fundamental Research Funds of Jilin University(No.SXGJQY2017-9,No.2017TD-19)the National Natural Science Foundation of China(No.61771219)
文摘DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms.
文摘The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.
基金Supported by "973" National Fundamental Research Program (51332)
文摘Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.
基金Supported by the National 863 Project (2003AA002030)
文摘One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61305110)the Self-Planned Task of Institute of Robotics(Grant No.F201803)the Intelligent Systems and Natural Science Foundation of Hubei Province(Grant No.2018CFB626).
文摘In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.
文摘随着海上风力发电和光伏发电的快速发展,海洋输电工程的地位越来越重要,海底电缆的应用也越来越广泛.获得精确的海底电缆位置不仅有利于日常巡检,而且提高了故障检测的效率,因此,海底电缆的路由定位和故障检测将会是未来维护和维修的重要环节.由于海底电缆的小直径和内部电流的变化性,导致定位准确度的下降以及定位难度的上升.针对上述问题,首先,基于海底环境和水下机器人,利用三芯铠装海底电缆的电缆结构推导海底电缆外磁场的近似方程;然后,水下机器人根据检测到的磁感应强度值进行姿态调整,在此基础上,提出一种基于改进灰狼优化算法(improved grey wolf optimization,IGWO)的海底电缆定位算法,利用基于磁通密度模的适应度函数,设计一种用于海底电缆探测的在线路径定位方法;最后,通过仿真实验验证了IGWO算法实现海底电缆定位的精确性和有效性.
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.
基金Supported by the National Natural Science Foundation of China(61903291)the Key R&D Project in Shaanxi Province(2022GY-134)+1 种基金the Open Fund Project of New Energy Joint Laboratory of China Southern Power Grid Corporation in 2022(GDXNY2022KF01)the China Southern Power Grid Laboratory Open Subject Fund Project(0304002022030103GD00037).
文摘A dynamic reconfiguration method for photovoltaic(PV)arrays based on an improved dung beetle algorithm(IDBO)to address the issue of PV array mismatch loss caused by partial shading conditions(PSCs)is proposed.To establish the output power-current(P-I)segmentation function for the total-cross-tied(TCT)PV array and the constraint function for the electrical switches,the IDBO algorithm was used to optimize both the P-I segmentation function and the electrical switch constraint function.IDBO is compared with algorithm-free reconfiguration and five other heuristic algorithms using two evaluation criteria:mismatch loss and power enhancement percentage,across six shading scenarios for 6x6 PV arrays.The irradiation distribution of PV arrays reconfigured by IDBO is also presented.The results show that IDBO effectively increases the output power of PV arrays and reduces mismatch loss.The output PV curves tend to exhibit a single peak,and the reconstruction results are superior to those obtained with the other methods.
