An operation risk analysis method based on feature association rule mining algorithm for power system with high proportion renewable energy

Abstract

The growth of renewable energy penetration increases the power randomness and fluctuations in power system and operation risk of the grid. The calculation efficiency of the conventional system static security risk analysis method based on probabilistic power flow is greatly reduced in the face of scenarios with a large number of random variables; so the online analysis function is restricted. In this paper, data mining method is applied to rapid analysis of operation risk of power grid with a high proportion of renewable energy. First, the potential relationship between the renewable energy characteristics and the static security risk characteristics of the power grid is mined through the FP-growth (Frequent Pattern growth) association analysis algorithm. Then, the association rule model that can be used for the real-time operation risk analysis of the high proportion of renewable energy power grid is constructed. Finally, the effectiveness and feasibility of the proposed method are verified by an example.