Research on Collaborative Observability Model of Multidimensional Data in Power Grid Based on User Feature Mining
The current collaborative observability model for power grid data mainly targets a single or a few observable sources,without considering multi-dimensional data in the power grid,resulting in significant deviation in observed data.Therefore,a multi-dimensional collaborative observability model for power grid data based on user feature mining is designed.After constructing the topology structure of the power grid,partition it,establish motor dynamics equations in different regions,obtain multi-dimensional data of the power grid,mine association rules on the obtained data,obtain frequent itemsets,and correct them.Combined with online analytical processing technology,realize the storage of multi-dimensional data.To verify the effectiveness of the model in practical applications,numerical examples were used to test the model.The results showed that the designed multi-dimensional data collaborative observability model for power grids had smaller observation data errors in different data types and higher fitting accuracy with actual power grid data,indicating good practicality.
User feature miningGrid multidimensional dataCollaborative observationModel design
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