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作 者:张志轩 李乐蒙 周宁 贾小凡 蒋哲 赵康 ZHANG Zhixuan;LI Lemeng;ZHOU Ning;JIA Xiaofan;JIANG Zhe;ZHAO Kang(State Grid Shandong Electric Power Research Institute,Jinan 250003,China;State Grid Shandong Electric Power Construction Company,Jinan 250118,China;School of Electrical Engineering,Shandong University,Jinan 250100,China)
机构地区:[1]国网山东省电力公司电力科学研究院,山东济南250003 [2]国网山东省电力公司建设公司,山东济南250118 [3]山东大学电气工程学院,山东济南250100
出 处:《山东电力技术》2024年第7期27-34,共8页Shandong Electric Power
基 金:国家自然科学基金项目“计及通信时滞和控制器参数不确定性的大规模电力系统小干扰稳定域研究”(52077126)。
摘 要:高比例新能源设备给电网带来电源不确定性增高和转动惯量变弱等问题,通过含新能源设备精细化模型的大电网仿真可拟合设备运行控制特性,校验控制策略,提前预防电网潜在风险,有效提高电网安全性。新能源设备模型的建模精度影响大电网仿真精度,为解决新能源设备模型的精度问题,利用贴近实际设备的半实物仿真平台开展高低电压穿越等工况仿真。通过使用ADPSS(advanced digital power system simulator,ADPSS)技术,利用相关基础元件建立直流卸荷回路,结合相关两电平换流器控制策略,搭建详细的单电机双功率单元两电平直驱风机半实物仿真电磁模型,并通过直接频率控制方法优化模型,从而增强了半实物仿真的稳定性,结果比对显示,直接频率控制方法优化模型降低了仿真的波动性,可有效提升仿真的准确性。A high proportion of new energy equipment brings problems such as increased power supply uncertainty and weakened rotational inertia to the power grid.By simulating a large-scale power grid with refined models of new energy equipment,the operational control characteristics of the equipment can be fitted,control strategies can be verified,potential risks of the power grid can be prevented in advance,and the safety of the power grid can be effectively improved.In order to solve the accuracy problem of the new energy equipment model,a semi-physical simulation platform close to the actual equipment was used to carry out high and low voltage ride through and other working condition simulations.By using ADPSS technology,a DC unloading circuit was established using relevant basic components.Combined with the relevant two-level converter control strategy,a detailed semiphysical simulation electromagnetic model of a single-motor dual-power unit two-level direct-drive fan was built.And by optimizing the model through direct frequency control method,the stability of semi-physical simulation was enhanced.The comparison of results shows that the direct frequency control method optimizing the model reduces the volatility of the simulation and greatly improves the accuracy of the simulation.
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