A cloud computing framework for cascading failure simulation and analysis of large-scale transmission systems

In practical industry applications, computing complexity is frequently the primary concern of transmission system cascading failure simulation (CFS), because of high-order contingency combinations and probabilistic time-sequenced events caused by the impacts of uncertainty. In this paper, a cloud computing framework based on Hadoop/MapReduce integrated with BPA software is presented for performing high-efficiency parallel CFS and analysis. The most significant functions for CFS, including automatic action logic identification, pre-defined fault set scanning, fault chain searching, and system severity evaluation, were also carefully designed to contribute to the analysis procedure as precisely as possible for both quasi-steady and dynamic analyses. The complete architecture is implemented using Java. Two benchmark cases and one real transmission system numeric test verifies the feasibility of the proposed technology.