Outlier Detection Method Based on Compressed Time Series and Voronoi Diagram for Power Data

doi:10.3969/j.issn.1000－7229.2017.05.014

Abstract

Abstract: The deep integration of information system and physical system made power system easily affected by outlier data, while the existing outlier detection methods for power system didnt take the advantages of data features, and had problems such as heavy computation, bad flexibility and low precision, etc. This paper proposes an outlier detection method based on compressed time series and Voronoi diagram, which adopts the time series extraction method of important points section to reduce the dimension of data in power system, map it to a two-dimensional plane, construct the Voronoi diagram partition, and then detect the abnormal data. This method can reduce the data dimension and algorithm complexity, set anomaly threshold according to the sequence features flexible, and realize the accurate detection of abnormal data. The simulation results have verified the effectiveness of the proposed method.

Key words: time series, Voronoi diagram, outlier detection, power data

CLC Number:

TM 73

PEI Tian, QI Donglian . Outlier Detection Method Based on Compressed Time Series and Voronoi Diagram for Power Data

[J]. Electric Power Construction, 2017, 38(5): 105-.

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