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作 者:庄一豪 薛志航 李红兵 谢强[1] ZHUANG Yihao;XUE Zhihang;LI Hongbing;XIE Qiang(College of Civil Engineering,Tongji University,Shanghai 200092,China;State Grid Sichuan Electric Power Research Institute,Chengdu 610041,China;State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430048,China)
机构地区:[1]同济大学土木工程学院,上海200092 [2]国网四川省电力公司电力科学研究院,成都610041 [3]国网湖北省电力有限公司,武汉430048
出 处:《高压电器》2025年第4期111-119,共9页High Voltage Apparatus
基 金:国家自然科学基金项目(51878508)。
摘 要:震害调查表明,强震作用下变电站有较高的易损性,对震后的快速修复及抢险救灾工作产生极大的影响。以220 kV变电站为分析案例,文中进行了系统震害的抽样模拟,并采用混合整数线性规划(MILP)生成了最优修复决策序列。利用MILP的决策结果,本研究训练了人工神经网络(ANN)模型,提出了基于该模型的修复策略,适用于缺乏高性能计算条件的场景。其次,分析了变电站震后各类组件的修复优先级,可实现灾后故障位置未知时的修复决策。最后,基于3种不同的改造策略对变电站进行了抗震韧性改造。结果表明,与MILP策略相比,基于ANN模型的修复策略能够显著降低计算成本,并保持修复决策的高效性。此外,给出了不同投入成本情况下的最优改造策略。这些结果验证了文中提出的修复策略和改造策略的有效性。The seismic disaster survey shows that substation has higher vulnerability under the strong seismic ac-tion,which significantly affects post-seismic disaster recovery and rescue efforts.In this paper,a 220 kV substation is taken as a case study to perform the sampling simulation of the system disaster,and the mixed integer linear pro-gramming(MILP)is used to generate optimal repair decision sequence.In this study,the MILP decision result is used to train an artificial neural network(ANN)model.The repair strategy based on the model is proposed and is suit-able for scenarios which is lack of high performance calculation condition.Then,the repair priorities for various com-ponents of substation after the seismic is analyzed,which can achieve the repair strategy in case of unknown fault po-sition after the disaster.Finally,the seismic resilience retrofitting of substation is performed by using three distinct retrofitting strategies.The result shows that compared to MILP strategy,the ANN-based repair strategy can significant-ly reduce computational costs while maintaining the efficiency of repair decisions.In addition,The optimal retrofit-ting strategy with different budget is given.The effectiveness of the repair strategies and retrofitting strategies pro-posed in this paper are verified by those results.
分 类 号:TM63[电气工程—电力系统及自动化]
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