A parallel heuristic reduction based approach for distribution network fault diagnosis

• We present a parallel attribute reduction approach based on MapReduce.
• We present a parallel approach for value reduction based on MapReduce.
• The experiments of parallel reduction in power systems fault diagnosis.

For the large volume of data in power systems, existing approaches to rough sets reduction either run on a single machine, or are paralleledly achieved in an approximate manner. They also seldom consider rough sets based value reduction. These problems restrict them in applications of power systems. In order to accelerate attribute reduction and value reduction, and improve the efficiency of fault diagnosis analysis in power systems, in this paper, we present a parallel heuristic approach to exact attribute reduction and value reduction for fault diagnosis in distribution network. We obtain the diagnosis rules and diagnose and locate the faults in the distribution network. Our parallel algorithms have been implemented on the MapReduce platform. The experimental results show that our method can effectively improve reduction process and improve the accuracy of reduction results in dealing with a large volume of data sets.