为了解决传统双向DC/DC变换器的损耗较大和效率较低等实际问题,提出了一种将交错并联磁集成技术与软开关技术相结合的控制方法。首先,以三相交错并联磁集成双向DC/DC变换器为研究实例,分析了当变换器分别运行在一、二、三相电感时的3种...为了解决传统双向DC/DC变换器的损耗较大和效率较低等实际问题,提出了一种将交错并联磁集成技术与软开关技术相结合的控制方法。首先,以三相交错并联磁集成双向DC/DC变换器为研究实例,分析了当变换器分别运行在一、二、三相电感时的3种工作状态。在每种状态下,分别讨论了电感反向电流的持续时间和死区时间,从而总结出了变换器运行在每种状态下可以实现零电压开关(zero voltage switch,ZVS)的条件。最后,通过实验进一步验证了理论分析的正确性,证实了该设计方案的实用性。展开更多
Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables.In practical application scenarios, however, the feedback cycle of target variable is usually larger...Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables.In practical application scenarios, however, the feedback cycle of target variable is usually larger than that of the process variables, which causes the deficiency of prediction errors. Consequently soft sensor cannot be calibrated timely and deteriorates. We proposed a soft sensor calibration method by using Just-in-time modeling and Ada Boost learning method. A moving window consisting of a primary part and a secondary part is constructed.The primary part is made of history data from certain number of constant feedback cycles of target variable and the secondary part includes some coarse target values estimated initially by Just-in-time modeling during the latest feedback cycle of target variable. The data set of the whole moving window is processed by Ada Boost learning method to build an auxiliary estimation model and then target variable values of the latest corresponding feedback cycle are reestimated. Finally the soft sensor model is calibrated by using the reestimated target variable values when the target feedback is unavailable; otherwise using the feedback value. The feasibility and effectiveness of the proposed calibration method is tested and verified through a series of comparative experiments on a pH neutralization facility in our laboratory.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
文摘为了解决传统双向DC/DC变换器的损耗较大和效率较低等实际问题,提出了一种将交错并联磁集成技术与软开关技术相结合的控制方法。首先,以三相交错并联磁集成双向DC/DC变换器为研究实例,分析了当变换器分别运行在一、二、三相电感时的3种工作状态。在每种状态下,分别讨论了电感反向电流的持续时间和死区时间,从而总结出了变换器运行在每种状态下可以实现零电压开关(zero voltage switch,ZVS)的条件。最后,通过实验进一步验证了理论分析的正确性,证实了该设计方案的实用性。
基金Supported by the National Basic Research Program of China(2012CB720500)
文摘Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables.In practical application scenarios, however, the feedback cycle of target variable is usually larger than that of the process variables, which causes the deficiency of prediction errors. Consequently soft sensor cannot be calibrated timely and deteriorates. We proposed a soft sensor calibration method by using Just-in-time modeling and Ada Boost learning method. A moving window consisting of a primary part and a secondary part is constructed.The primary part is made of history data from certain number of constant feedback cycles of target variable and the secondary part includes some coarse target values estimated initially by Just-in-time modeling during the latest feedback cycle of target variable. The data set of the whole moving window is processed by Ada Boost learning method to build an auxiliary estimation model and then target variable values of the latest corresponding feedback cycle are reestimated. Finally the soft sensor model is calibrated by using the reestimated target variable values when the target feedback is unavailable; otherwise using the feedback value. The feasibility and effectiveness of the proposed calibration method is tested and verified through a series of comparative experiments on a pH neutralization facility in our laboratory.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
