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Three-way unsupervised data mining for power system applications based on tensor decomposition
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DOI：10.1016/j.epsr.2020.106431.png)
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Abstract
En 中文
Sophisticated geospatial metering devices used in today's networks such as the advanced metering infrastructure (AMI), wide area measurement system (WAMS) and supervisory control and data acquisition (SCADA) open new opportunities to monitor the security of the system in real time. Consequently, these metering infrastructures have received significant attention in recent years from data mining communities because of the new challenges involved on managing this information. One of the main challenges is the analysis of multivariable data, which represents datasets containing variables of different nature, which are linked. In this document a data mining technique that allows the analysis of multivariate data is presented. Moreover, an innovative application of an unsupervised data mining algorithm for smart meters data, particularly to Electrical Load Profile using tensor decomposition is presented. Since the proposed tensor representation allows to assign a given dimension to a particular variable involved; data reduction, data compression, data visualization and data clustering is archived separately for every variable. To validate the effectiveness of the proposed methodology, a three-way tensor built with data from the Electrical Reliability Council of Texas (ERCOT) is presented. The results demonstrate that is possible to extract more information than using conventional approaches based on 2-way arrangements (matrices). Additionally, the proposed algorithm is solved using an iterative approach, which indirectly enable to estimate missing data.
Keywords:
Three-way tensor decomposition
PARAFAC
Clustering
Compression
Missing data
Electrical load data
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