By taking into account the multi-dimensional

hybrid spatial and temporal characteristics of data from a power system and/or a power information system

a big data attribute reduction preprocessing method based on partial order reduction is presented. In the proposed method

the parallel advantage of MapReduce is employed and the independence of concurrent events is focused on

making it possible to meet the coverage requirements of big data attribute dimension and reduction in a power system. Finally

the proposed attribute reducing method is simulated by some examples including the monitoring data of a photovoltaic (PV) power generation system

the fault diagnosis data of a transformer

and real-time and reliability data of the communication system of an intelligent substation

with good performance observed through a Hadoop platform. This work is supported by National Natural Science Foundation of China (No. 51477151

No. 51407076)

Hebei Provincial Natural Science Foundation of China (No. F2014502050)

the Higher Education Scientific Research Project of Hebei Province (No. Z2013007)

and Fundamental Research Funds for the Central Universities (No. 2015ZD28).