Phase preserving profile generation from measurement data by clustering and performance analysis: a tool for network planning and operation

IF 2.4 Q1 Computer Science
Paul Zehetbauer, Matthias Stifter, Bharath Varsh Rao
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引用次数: 5

Abstract

The need for improved operational efficiency planning accuracy leads to a growing number of sensors and other monitoring sources in our power system. New methods for properly dealing with this increasing amount of data are required. This paper presents how clustering can help to drastically reduce the processing time of energy data time series. The developed approach categorizes similar load behavior by means of agglomerative hierarchical clustering based on their correlation coefficient. It includes the determination of the best number of clusters to model different load patterns with respect to the total error given as a key performance indicator. The results are a reduced set of representative three phase load profiles based on the data input and clustering configurations. The accuracy of these representative profiles is validated by resembling the original data set. Dependent on available computational resources a network operator can use this to intelligently compress measurement data while keeping the required accuracy. The method is demonstrated on data from the testbed of Aspern Smart City Research in Seestadt Aspern, Austria.
通过聚类和性能分析从测量数据生成相位保持剖面:网络规划和运行的工具
由于需要提高运行效率和规划精度,我们的电力系统中需要越来越多的传感器和其他监控源。需要新的方法来正确处理这种不断增加的数据量。本文介绍了聚类是如何帮助大幅度减少能源数据时间序列的处理时间。该方法采用基于相关系数的聚类分层聚类方法对相似负荷行为进行分类。它包括根据作为关键性能指标的总误差确定用于模拟不同负载模式的最佳集群数量。结果是基于数据输入和聚类配置的具有代表性的三相负荷曲线的简化集。通过与原始数据集的相似度验证了这些代表性轮廓的准确性。根据可用的计算资源,网络运营商可以使用它来智能地压缩测量数据,同时保持所需的精度。该方法在奥地利Seestadt Aspern的Aspern智能城市研究试验台的数据上进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
SICS Software-Intensive Cyber-Physical Systems
SICS Software-Intensive Cyber-Physical Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-
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