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Techniques for Maintaining Situational Awareness During Large-Scale Electric Grid Simulations

机译:在大型电网模拟中保持态势意识的技术

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Simulations of the dynamic response of high voltage, large-scale electric grids often generate large amounts of data. This can make it difficult for engineers to understand the overall system behavior. This paper presents various techniques to help with gaining situational awareness for electric grid simulations in the time frame of milliseconds to minutes. These techniques include the use of time-domain graphs, geographic data views in which geographic information embedded in the electric grid model is leveraged to create visualizations, contouring, animation loops, machine learning and modal analysis. Results are demonstrated on a 10,000 bus synthetic grid model and an actual electric grid model with 110,000 buses.
机译:仿真高电压的动态响应,大型电网通常会产生大量数据。 这可能使工程师难以理解整体系统行为。 本文介绍了各种技术,以帮助在毫秒的时间框架中获得对电网模拟的态势意识。 这些技术包括使用时域图,其中嵌入在电网模型中的地理信息的地理数据视图被利用以创建可视化,轮廓,动画回路,机器学习和模态分析。 结果在10,000总线合成电网模型和具有110,000辆公交车的实际电网模型上进行了演示。

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