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.
The work was supported by National Natural Science Foundation of China. (No. 60574079 and 50507017), Supported by Zhejiang Provincial Natural Science Foundation of China. (No. 601112)
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Meng, J., Sun, D., Li, Z. (2006). Applications of Data Mining Time Series to Power Systems Disturbance Analysis. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_82
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DOI: https://doi.org/10.1007/11811305_82
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