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Multi-Objective Approach for Optimizing Production Parameters of Low-Permeability Oil Well to Enhance Energy Efficiency 认领 引用
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作者 LIU Peijin DING Haojian +3 位作者 YAN Dongyang SUN Haofeng HUANG Tao LI Jie 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期486-498,共13页
Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of product... Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters,this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency.First,a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established,and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost,to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model.Second,a multi-objective optimization model was established by taking into account the decision variables and constraints of the model,to maximize the daily liquid production yield while minimizing the unit production energy consumption cost.Subsequently,the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters.Finally,the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield.The results showed an increase in oil well production yield,and a significant energy-saving effect,thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper. 展开更多
关键词 low-permeability oil well production parameter model error compensation multi-objective optimization non-dominated genetic algorithm
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Photovoltaic GMPPT Control Method under Local Shade Based on Improved DBO 认领 引用
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作者 Peijin Liu Tao Huang +2 位作者 Haojian Ding Lei Dong Jie Li 《Chinese Journal of Electrical Engineering》 EI CSCD 2025年第4期243-257,共15页
In order to further improve the tracking accuracy,speed,and disturbance robustness of the global maximum power point tracking(GMPPT)control of a photovoltaic array under partial-shade conditions,a photovoltaic GMPPT c... In order to further improve the tracking accuracy,speed,and disturbance robustness of the global maximum power point tracking(GMPPT)control of a photovoltaic array under partial-shade conditions,a photovoltaic GMPPT control method based on the improved dung beetle optimization(IDBO)algorithm is proposed.First,in order to improve the algorithm performance,a Chebyshev chaotic map is used to initialize the positions of the dung beetles to make the distribution of the dung beetle population in the search space more uniform,which increased the population convergence rate and final solution accuracy of the algorithm.Second,a neighborhood search mechanism combined with a Levy flight strategy is introduced to enhance the local search precision and convergence speed of the algorithm,and improve the accuracy of the global maximum power point(GMPP)location.At the same time,a dynamic weight is introduced to improve the convergence rate of the algorithm in the later search stage,along with the global-search ability of the equalization algorithm.Finally,a restart mechanism is used to enhance the robustness of the GMPPT control through the influence of mutation factors in a complex environment.The experimental results show that compared with DBO,grey wolf optimization(GWO),sparrow search algorithm(SSA)and particle swarm optimization(PSO)algorithms,the IDBO based solar maximum power point tracking control method is more accurate in determining the GMPP position,and has better dynamic response speed and tracking accuracy. 展开更多
关键词 Photovoltaic power generation partial shading global maximum power point tracking(GMPPT) IDBO algorithm restart mechanism
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