Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori...Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.展开更多
为了针对性地制定后续优化措施,以降低多机场终端区内航班延误所带来的不利影响,并提高多机场系统内各机场的运营效率,进行多机场终端区航班延误的预测研究。首先,考虑多机场终端区交通态势对航班延误的影响,在对多机场终端区交通态势...为了针对性地制定后续优化措施,以降低多机场终端区内航班延误所带来的不利影响,并提高多机场系统内各机场的运营效率,进行多机场终端区航班延误的预测研究。首先,考虑多机场终端区交通态势对航班延误的影响,在对多机场终端区交通态势进行分析的基础上,建立6个描述终端区交通态势的指标。接着,构建反向传播(back propagation,BP)神经网络航班延误预测模型,将终端区交通态势指标、航班信息和天气环境数据等作为输入,航班延误时间作为输出,并利用粒子群优化算法(particle swarm optimization,PSO)优化BP神经网络进行训练。通过实例验证和分析,基于多机场终端区交通态势的航班延误预测能够有效提高预测准确率,同时,通过粒子群优化BP神经网络的预测模型预测准确率均高于一般的考虑交通态势的BP和遗传算法优化的BP神经网络模型(genetic algorithm and back propagation,GA-BP)。展开更多
优化灌区渠系输配水技术是推动农业水资源高效利用的重要举措。针对新疆部分灌区渠系管理上沿用人工传递信息方法来决策配水方案,难以达到优化调配。以轮灌分组和配水流量为决策变量,建立了以渠道输水损失最小、轮灌组内配水时间差最小...优化灌区渠系输配水技术是推动农业水资源高效利用的重要举措。针对新疆部分灌区渠系管理上沿用人工传递信息方法来决策配水方案,难以达到优化调配。以轮灌分组和配水流量为决策变量,建立了以渠道输水损失最小、轮灌组内配水时间差最小为目标的灌区支、斗渠优化配水模型,采用多目标粒子群算法进行求解;在深入研究渠系优化配水模型及其算法求解的基础上,采用Visual Studio Code、Matlab开发工具,开发灌区渠系水优化配置系统,并通过实例进行检验分析。结果表明:优化后的配水方案较该时段实际灌溉方案,渗漏损失总量由48.49万m^(3)减少至23.78万m^(3),配水时间由30 d缩短为14.6 d。所建立的渠系优化配水模型贴近渠系实际运行情况,可以实现集中高效配水;开发的渠系水优化配置系统界面友好、参数简洁,能方便快速地为灌区的配水优化编组提供决策依据。展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
基金supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province(Grant No.181RTSTHN009)the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province(Grant No.2017016).
文摘Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
文摘为了针对性地制定后续优化措施,以降低多机场终端区内航班延误所带来的不利影响,并提高多机场系统内各机场的运营效率,进行多机场终端区航班延误的预测研究。首先,考虑多机场终端区交通态势对航班延误的影响,在对多机场终端区交通态势进行分析的基础上,建立6个描述终端区交通态势的指标。接着,构建反向传播(back propagation,BP)神经网络航班延误预测模型,将终端区交通态势指标、航班信息和天气环境数据等作为输入,航班延误时间作为输出,并利用粒子群优化算法(particle swarm optimization,PSO)优化BP神经网络进行训练。通过实例验证和分析,基于多机场终端区交通态势的航班延误预测能够有效提高预测准确率,同时,通过粒子群优化BP神经网络的预测模型预测准确率均高于一般的考虑交通态势的BP和遗传算法优化的BP神经网络模型(genetic algorithm and back propagation,GA-BP)。
文摘优化灌区渠系输配水技术是推动农业水资源高效利用的重要举措。针对新疆部分灌区渠系管理上沿用人工传递信息方法来决策配水方案,难以达到优化调配。以轮灌分组和配水流量为决策变量,建立了以渠道输水损失最小、轮灌组内配水时间差最小为目标的灌区支、斗渠优化配水模型,采用多目标粒子群算法进行求解;在深入研究渠系优化配水模型及其算法求解的基础上,采用Visual Studio Code、Matlab开发工具,开发灌区渠系水优化配置系统,并通过实例进行检验分析。结果表明:优化后的配水方案较该时段实际灌溉方案,渗漏损失总量由48.49万m^(3)减少至23.78万m^(3),配水时间由30 d缩短为14.6 d。所建立的渠系优化配水模型贴近渠系实际运行情况,可以实现集中高效配水;开发的渠系水优化配置系统界面友好、参数简洁,能方便快速地为灌区的配水优化编组提供决策依据。
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
