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作 者:陈炜 吴晓文 袁发庭 曾继皓 肖威 孙静玲 CHEN Wei;WU Xiaowen;YUAN Fating;ZENG Jihao;XIAO Wei;SUN Jingling(School of Information and Electrical Engineering,Hunan University of Science and Technology,Xiangtan,411201,China;College of Electrical and New Energy,China Three Gorges University,Yichang,Hubei,443002,China)
机构地区:[1]湖南科技大学信息与电气工程学院,湖南湘潭411201 [2]三峡大学电气与新能源学院,湖北宜昌443002
出 处:《智慧电力》2024年第4期116-124,共9页Smart Power
基 金:国家自然科学基金资助项目(52307179)。
摘 要:为了解决干式铁心电抗器多目标优化设计存在的不足,提出一种基于灵敏度分析的分层优化方法。首先,根据铁心电抗器的结构参数建立仿真模型,获得了电抗器电磁、温升和振动特性,在此基础上,基于多物理场仿真和拉丁超立方试验设计方法,结合灵敏度分析技术,得出结构参数对电抗器各性能目标的影响规律;其次,建立了铁心电抗器多目标优化模型,将优化目标与结构参数划分为2个层次,进行降维处理;最后,采用响应面法(RSM)和NSGA-Ⅱ算法分别获得了分层后最佳的铁心和绕组结构参数,并通过仿真验证优化方法的有效性。结果表明该方法能有效提高干式铁心电抗器的各项性能同时控制成本和损耗,可为铁心电抗器向质量效益型和绿色低碳型转变提供借鉴和参考。In order to solve the shortcomings of multi-objective optimization design of dry core reactor,a hierarchical optimization method based on sensitivity analysis is proposed.Firstly,a simulation model is established according to the structural parameters of the core reactor,and the electromagnetic,temperature rise and vibration characteristics of the reactor are obtained.On this basis,the influence rules of structural parameters on the performance targets of the reactor is obtained based on the multi-physics simulation and Latin Hypercube test design method combined with the sensitivity analysis technology.Secondly,the multi-objective optimization model of the core reactor is established,which divides the optimization objectives and structural parameters into two levels to perform dimensionality reduction.Finally,the optimal core and winding structure parameters after stratification are obtained using the RSM response surface method and NSGA-II algorithm respectively,and simulation results are provided to verify the effectiveness of the optimization method.The results show that this method can effectively improve the performance of the dry core reactor while controlling costs and losses..The above optimization method can provide reference for the transformation of the core reactor to the quality benefit type with the green and low-carbon type.
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