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.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.