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Applications of Data Mining Time Series to Power Systems Disturbance Analysis

  • Jun Meng
  • Dan Sun
  • Zhiyong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

In the last decade there has been an explosion of interest in mining time series data, introducing new algorithms to index, classify, cluster and segment time series. In this paper we use fractal theory and reconstructed phase space to analysis the special time series –power systems disturbance signal. After analyzing the feasible method of time series data mining-fractal theory and reconstructed phase space, which is used for the analysis of power disturbance signals. Eight common happed disturbances are considered in this paper, the simulation results show that fractal method can detect the transient disturbance, accurately locate the time when it occurred. Reconstructed phase space can classify the different type of disturbance. It is concluded that two methods are all efficient and intuitionistic for detection and diagnosis the fault of power system, which presents a new concept for power disturbance analysis.

Keywords

Fractal Dimension Power System Discrete Wavelet Transform Fractal Number Power Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Meng
    • 1
  • Dan Sun
    • 1
  • Zhiyong Li
    • 1
  1. 1.College of Electrical EngineeringZhejiang UniversityHangzhouChina

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