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Fast power system voltage prediction using knowledge-based approach and on-line box data creation

机译:使用基于知识的方法和在线盒数据创建快速电力系统电压的预测

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The author presents an algorithm for fast power system contingency analysis using pattern recognition. The pattern space is clustered into data boxes for each of search and flexibility of programming. The addressable memory structure of the pattern space decouples the voltage prediction problem into subproblems for one busbar and one contingency at a time. To reduce the working memory, the algorithm learns from the short-term load trend and selects only a few data boxes for on-line processing. Further computational savings and improvements in accuracy are achieved by adopting an innovative wafer data box structure that also facilitates subsequent refinement by additions/deletions of patterns acquired from real-time operational experience.
机译:作者提出了一种使用模式识别的快速电力系统应变分析算法。对于搜索和编程的灵活性,模式空间都聚集在数据框中。模式空间的可寻址存储结构将电压预测问题分解为一个母线和一个偶然性的子问题。为了减少工作内存,该算法从短期负载趋势中学习,并仅选择几个数据框进行在线处理。通过采用创新的晶圆数据盒结构,进一步节省了计算并提高了精度,该晶圆数据盒结构还通过添加/删除从实时操作经验中获取的图案来促进后续优化。

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