Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usa...Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not.However,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method.In order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this paper.At first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is proposed.Then,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model.Finally,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of GCA.The proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.展开更多
Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem ...Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.展开更多
为了解决南方某330 MW亚临界机组供热改造后一次调频性能较差,无法满足电网细则标准的问题,本文对其一次调频控制策略进行了全面、深入分析,从汽轮机阀门流量特性、汽轮机数字电液控制系统(digital electro-hydraulic control system,D...为了解决南方某330 MW亚临界机组供热改造后一次调频性能较差,无法满足电网细则标准的问题,本文对其一次调频控制策略进行了全面、深入分析,从汽轮机阀门流量特性、汽轮机数字电液控制系统(digital electro-hydraulic control system,DEH)侧调频函数、协调控制系统(coordinated control system,CCS)侧控制策略等影响一次调频调节品质的多个方面展开研究:通过汽轮机阀门流量试验,修正其流量特性函数;优化机组DEH侧和CCS侧一次调频控制回路结构和控制参数,完善机组一次调频功能。结果表明:机组一次调频动作正确率由86.4%提升至93.9%,动态调节准确性、快速性和稳定性显著改善,不同负荷工况下调频响应时间均低于3 s,15 s内调频质量均大于75%,过渡时间低于60 s,满足区域电网两个细则标准。该研究对火电机组供热改造后一次调频控制策略优化具有指导意义。展开更多
基金the Scientific Research Funding Project of Liaoning Education Department of China under Grant No.JDL2020005,No.LJKZ0485the National Key Research and Development Program of China under Grant No.2018YFA0704605.
文摘Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not.However,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method.In order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this paper.At first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is proposed.Then,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model.Finally,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of GCA.The proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.
文摘Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.
文摘为了解决南方某330 MW亚临界机组供热改造后一次调频性能较差,无法满足电网细则标准的问题,本文对其一次调频控制策略进行了全面、深入分析,从汽轮机阀门流量特性、汽轮机数字电液控制系统(digital electro-hydraulic control system,DEH)侧调频函数、协调控制系统(coordinated control system,CCS)侧控制策略等影响一次调频调节品质的多个方面展开研究:通过汽轮机阀门流量试验,修正其流量特性函数;优化机组DEH侧和CCS侧一次调频控制回路结构和控制参数,完善机组一次调频功能。结果表明:机组一次调频动作正确率由86.4%提升至93.9%,动态调节准确性、快速性和稳定性显著改善,不同负荷工况下调频响应时间均低于3 s,15 s内调频质量均大于75%,过渡时间低于60 s,满足区域电网两个细则标准。该研究对火电机组供热改造后一次调频控制策略优化具有指导意义。
