The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consump...The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.展开更多
In this paper, we study optimal recovery （reconstruction） of functions on the sphere in the average case setting. We obtain the asymptotic orders of average sampling numbers of a Sobolev space on the sphere with a G...In this paper, we study optimal recovery （reconstruction） of functions on the sphere in the average case setting. We obtain the asymptotic orders of average sampling numbers of a Sobolev space on the sphere with a Gaussian measure in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞, and show that some worst-case asymptotically optimal algorithms are also asymptotically optimal in the average case setting in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞.展开更多
This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Gen...This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Genetic Algorithm is a probabilistic search algorithm which is substantially used as an optimization technique in power electronics. A bunch of modifications have already been introduced to enhance the performance depending upon the applications. However, in this paper, modified genetic algorithm has been used in order to tune the key parameters in the converter. Hence, an analysis is carried out where the performance of the converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented. Responses of the overall system are accumulated through rigorous simulation in MATLAB environment.展开更多
【目的】为及时发现海上风电机组发电机轴承的故障,提出一种基于蜣螂优化(Dung Beetle Optimizer,DBO)算法和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)模型的DBO-XGBoost发电机轴承温度预测模型,并结合指数加权移动平均值(Exp...【目的】为及时发现海上风电机组发电机轴承的故障,提出一种基于蜣螂优化(Dung Beetle Optimizer,DBO)算法和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)模型的DBO-XGBoost发电机轴承温度预测模型,并结合指数加权移动平均值(Exponentially Weighted Moving Average,EWMA)控制图实现发电机轴承的故障预测。【方法】首先,通过最大互信息系数(Maximal Information Coefficient,MIC)选取数据采集与监视控制(Supervisory Control And Data Acquisition,SCADA)系统中能准确表征发电机轴承状态的关键特征,并将其输入DBO-XGBoost模型中,对正常工况下的发电机轴承温度进行预测。其次,使用马氏距离(Mahalanobis Distance,MD)衡量真实值与预测值之间的偏差,并将MD序列输入基于EWMA控制图的变点检测算法中,以获取故障出现的变点,从而实现故障预测。最后,基于特征的重要性构建轴承故障模式知识图谱。【结果】结果表明,所提方法能对正常工况下发电机轴承的温度实现较为精准的预测,并能提前3天对故障进行预警,与通过设定单一阈值进行故障预警的方法相比,所提方法能更准确地检测到故障发生的时间。构建的轴承故障模式知识图谱为运维人员提供了可视化的运维决策支持。展开更多
The solutions of Linear Programming Problems by the segmentation of the cuboidal response surface through the Super Convergent Line Series methodologies were obtained. The cuboidal response surface was segmented up to...The solutions of Linear Programming Problems by the segmentation of the cuboidal response surface through the Super Convergent Line Series methodologies were obtained. The cuboidal response surface was segmented up to four segments, and explored. It was verified that the number of segments, S, for which optimal solutions are obtained is two (S = 2). Illustrative examples and a real-life problem were also given and solved.展开更多
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization...Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.展开更多
In this paper a new method has been proposed to decide optimal placement and best sizing of STATCOM （static synchronous compensator）. The best place of STATCOM is found using the sensitivity analysis and optimum siz...In this paper a new method has been proposed to decide optimal placement and best sizing of STATCOM （static synchronous compensator）. The best place of STATCOM is found using the sensitivity analysis and optimum sizing of STATCOM is managed using the genetic algorithm. The average model can account for the high-frequency effects and power electronic losses, and more accurately predict the active and reactive power outputs of the STATCOM. This paper employs the DIgSILENT simulator and DPL （DIgSILENT programming language） as a programming tool of the DIgSILENT to show the validity of the proposed method. The effectiveness of suggested approach has been tested on part of the distribution network of Iran, Khoramdarreh city in Zanjan province.展开更多
正交时频空(orthogonal time frequency space,OTFS)调制在高多普勒环境下可实现可靠通信,适用于卫星通信等高动态场景。然而,其峰值与平均功率比(peak-to-average power ratio,PAPR)易超出功放线性范围,导致非线性失真。部分传输序列(p...正交时频空(orthogonal time frequency space,OTFS)调制在高多普勒环境下可实现可靠通信,适用于卫星通信等高动态场景。然而,其峰值与平均功率比(peak-to-average power ratio,PAPR)易超出功放线性范围,导致非线性失真。部分传输序列(partial transfer sequence,PTS)算法通过对数据符号分块再选取合适的旋转因子可以抑制PAPR。为提高PTS算法抑制PAPR的能力,提出了一种基于改进的灰狼优化(improved grey wolf optimizer,IGWO)算法的PTS算法,即IGWO-PTS算法,以适应离散组合优化问题并获得更优的子块划分方案,从而获得更好的PAPR抑制能力。推导证明了当相位旋转因子集合元素具有旋转对称性时,相位旋转因子组合空间可以收缩为原来的1 K(K为集合中元素个数),极大程度上降低了系统复杂度。仿真实验表明,IGWO-PTS算法相对于传统算法具有更好的PAPR抑制性能,并且在星地高动态场景下可以保持良好的传输可靠性。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52375264 and 62376212).
文摘The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
基金supported by the National Natural Science Foundation of China(No.11426179)the National Natural Science Foundation of China(Nos.10871132,11271263)+4 种基金the Key Scientific Research Fund of Xihua University(No.z1312624)the Foundation of Sichuan Educational Committee(No.14ZA0112)the Preeminent Youth Fund for School of Science in Xihua Universitythe Beijing Natural Science Foundation(No.1132001)BCMIIS
文摘In this paper, we study optimal recovery （reconstruction） of functions on the sphere in the average case setting. We obtain the asymptotic orders of average sampling numbers of a Sobolev space on the sphere with a Gaussian measure in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞, and show that some worst-case asymptotically optimal algorithms are also asymptotically optimal in the average case setting in the Lq （S^d-1） metric for 1 ≤ q ≤ ∞.
文摘This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Genetic Algorithm is a probabilistic search algorithm which is substantially used as an optimization technique in power electronics. A bunch of modifications have already been introduced to enhance the performance depending upon the applications. However, in this paper, modified genetic algorithm has been used in order to tune the key parameters in the converter. Hence, an analysis is carried out where the performance of the converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented. Responses of the overall system are accumulated through rigorous simulation in MATLAB environment.
文摘【目的】为及时发现海上风电机组发电机轴承的故障,提出一种基于蜣螂优化(Dung Beetle Optimizer,DBO)算法和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)模型的DBO-XGBoost发电机轴承温度预测模型,并结合指数加权移动平均值(Exponentially Weighted Moving Average,EWMA)控制图实现发电机轴承的故障预测。【方法】首先,通过最大互信息系数(Maximal Information Coefficient,MIC)选取数据采集与监视控制(Supervisory Control And Data Acquisition,SCADA)系统中能准确表征发电机轴承状态的关键特征,并将其输入DBO-XGBoost模型中,对正常工况下的发电机轴承温度进行预测。其次,使用马氏距离(Mahalanobis Distance,MD)衡量真实值与预测值之间的偏差,并将MD序列输入基于EWMA控制图的变点检测算法中,以获取故障出现的变点,从而实现故障预测。最后,基于特征的重要性构建轴承故障模式知识图谱。【结果】结果表明,所提方法能对正常工况下发电机轴承的温度实现较为精准的预测,并能提前3天对故障进行预警,与通过设定单一阈值进行故障预警的方法相比,所提方法能更准确地检测到故障发生的时间。构建的轴承故障模式知识图谱为运维人员提供了可视化的运维决策支持。
文摘The solutions of Linear Programming Problems by the segmentation of the cuboidal response surface through the Super Convergent Line Series methodologies were obtained. The cuboidal response surface was segmented up to four segments, and explored. It was verified that the number of segments, S, for which optimal solutions are obtained is two (S = 2). Illustrative examples and a real-life problem were also given and solved.
基金appreciation to King Saud University for funding this research through the Researchers Supporting Program number(RSPD2024R918),King Saud University,Riyadh,Saudi Arabia.
文摘Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.
文摘In this paper a new method has been proposed to decide optimal placement and best sizing of STATCOM （static synchronous compensator）. The best place of STATCOM is found using the sensitivity analysis and optimum sizing of STATCOM is managed using the genetic algorithm. The average model can account for the high-frequency effects and power electronic losses, and more accurately predict the active and reactive power outputs of the STATCOM. This paper employs the DIgSILENT simulator and DPL （DIgSILENT programming language） as a programming tool of the DIgSILENT to show the validity of the proposed method. The effectiveness of suggested approach has been tested on part of the distribution network of Iran, Khoramdarreh city in Zanjan province.
文摘正交时频空(orthogonal time frequency space,OTFS)调制在高多普勒环境下可实现可靠通信,适用于卫星通信等高动态场景。然而,其峰值与平均功率比(peak-to-average power ratio,PAPR)易超出功放线性范围,导致非线性失真。部分传输序列(partial transfer sequence,PTS)算法通过对数据符号分块再选取合适的旋转因子可以抑制PAPR。为提高PTS算法抑制PAPR的能力,提出了一种基于改进的灰狼优化(improved grey wolf optimizer,IGWO)算法的PTS算法,即IGWO-PTS算法,以适应离散组合优化问题并获得更优的子块划分方案,从而获得更好的PAPR抑制能力。推导证明了当相位旋转因子集合元素具有旋转对称性时,相位旋转因子组合空间可以收缩为原来的1 K(K为集合中元素个数),极大程度上降低了系统复杂度。仿真实验表明,IGWO-PTS算法相对于传统算法具有更好的PAPR抑制性能,并且在星地高动态场景下可以保持良好的传输可靠性。
