Abstract:In the power system risk assessment research, traditional methods have shortcomings such as long processing time and lack of real-time interaction performance. It is difficult to comprehensively reflect operating status of the grid. This paper proposes a big data architecture method based on Hash algorithm to improve the effectiveness of power system risk assessment. The Hash algorithm is used to correlate the basic data of the power system to the service platform and the risk assessment system. The overhead line outage model and the transformer time-varying outage model are used to model the big data of the power system, and the static and transient security risk indicators are integrated. On the basis of multi-dimensional data sources, the power system risk assessment associated big data analysis system was formed. This method effectively solves the diversity and uncertainty involved in power system risk assessment and improves the effectiveness of risk assessment. Apply it to the annual risk assessment of a regional power grid in Chongqing, and the evaluation results obtained are in line with the actual situation. It is verified that the method can comprehensively reflect the evolution of the state of the grid equipment and the external environment changes of the grid, and improve the accuracy and real-time of the risk assessment.