Abstract
The development of power simulation artificial intelligence technology needs massive open sample data. It is a trend to use the characteristics of online data to construct sample data. In order to solve the problem that the online data of power grid is rich in information, but the utilization rate of features is not high, aiming at the information features of generators, the LTTB dimension reduction and DBSCAN + L2 clustering methods are proposed, which reduce the complexity of feature extraction of time series data. The method is verified by the actual power grid data, and has achieved certain results.
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