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A novel approach to create historical powerflow cases from PI data using open source software

机译:使用开源软件从PI数据创建历史动力流例的新方法

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This paper discusses a novel procedure by which historical powerflow cases (HPCs) are created using open-source tools to mitigate deficiencies in previous documented methods. The procedure described in this paper is completely automated and performed entirely in Python to retrieve data from the historian of the author’s utility (PI SystemTM by OSIsoft®) and perform all commands in the powerflow software of the author’s utility (PSS® E by Siemens PTI). Regular expressions are used to dynamically assign PI tags from the author’s PI server to each relevant component in a PSS® E powerflow case. Validations of the retrieved PI data and the HPC itself are automatically performed by the procedure. This paper compares solved branch powerflows with the PI data which they are supposed to represent by running the procedure 100 times on random timestamps in one calendar year. 95% of the branch errors are less than 25.0 MW and the mean branch error is less than 1.0 MW. 70 seconds of computation time is required to create an HPC using the approach described in this paper which includes all PSS® E manipulations, downloads from the PI server, and validations.
机译:本文讨论了一种新颖的程序,通过开源工具创建了历史动力流例(HPC)以在以前的文档方法中减少缺陷。本文描述的过程完全自动化,完全在Python中执行,以检索来自作者实用程序的历史记录(PI系统的数据) tm 通过ofoyoft.®)并在作者实用程序的PowerFlow软件中执行所有命令(PSS® 通过西门子PTI)。正则表达式用于将来自作者PI服务器的PI标签动态分配给PSS中的每个相关组件® E PowerFlow外壳。通过过程自动执行检索到的PI数据和HPC本身的验证。本文将索明的分支能量与PI数据进行了比较,这些PI数据应该通过在一个日历年的随机时间戳上运行100次来表示它们的PI数据。 95%的分支误差小于25.0 mw,平均分支误差小于1.0 mw。需要使用本文中描述的方法创建HPC所需的70秒计算时间,包括所有PSS® e操纵,从PI服务器下载和验证。

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