Power system disaster-mitigating dispatch platform based on big data

Based on the big data technique, this paper proposes an implementation scheme of the Disaster-Mitigating Dispatch Platform (DMDP), which integrates the functions of dynamic risk awareness, comprehensive analysis, decision making and multi-dimensional visualization associated with weather and natural disasters. First, the framework of the DMDP based on Hadoop 2.0 is presented. Then, the demand of mass, multisource and heterogeneous data, such as structured, semi-structured and unstructured data, for power systems disaster preventing is analyzed. The functions for disaster mitigating could be divided in three stages: prior to the disaster, DMDP performs big data mining based on history database, dispatches human resources and materials and formulates preventive measures for contingencies. During the disaster process, DMDP monitors conditions of the equipment, system and environment. Once a major contingency occurs, DMDP can alarm, identify and give corrective actions. After the disaster, the failure will be investigated and the efficiency of disaster preventing will be assessed. The proposed DMDP could serve as a useful decision-making tool for the operators.