The data from power grid not only have the feathers of multi-source

high-dimension

prior and hierarchical structure

but also contain enormous characteristics of causal relationship. It is the hotspot issue in practical manufacture and management of power grid monitoring system

which involves how to figure out causal relation models in different practical scenarios from historical big data of power grid

and to establish monitoring operation business. According to causal relation characteristics of big data for power grid

this paper proposes a novel intelligent data mining framework based on causal relative probabilistic graphical models. Through the combination of correlation analysis and Granger causal analysis

this framework can abstract correlative deductive mechanics of causal factor sets from enormous multi-sourced multi-dimensional raw data

and generate causal relative probabilistic graphical models of monitoring big data. After that

a construction method of application model for big data monitored by power grid with two stages is proposed. In the first phase

the big data analysis approaches are used to work out the variable set with strong causal relationship from mass multi-sourced and multi-dimensional data. In the second phase

the combination of expertise and recommended models is leveraged to filter out the set of functions so as to generate the business models of monitoring. The first big data real-time monitoring system of China

which is based on above methods

has already operated in five provincial control centers

and the final operating results validate the effectiveness of the proposed method.