Power System Stability Estimation Scheme Based on Big Data

Abstract

Aiming at the poor convergence of power flow calculation and the difficulty in location of the power system operation, this paper proposes a fast parallel calculation scheme for power system stability assessment based on big data technology. Through the analysis and research of Newton method which is sensitive to the initial value selection in the actual calculation of regional power flow, an approach for selecting the optimal initial value is proposed by genetic algorithm. Then, by the state sampling method and Monte Carlo parallel simulation, the coarse granularity algorithm under the message passing programming model is designed. The experimental analysis adopts the improved method to test the data in IEEE 9 system, actual environment, the automatic power flow adjustment. The results show that our scheme can achieve high parallel efficiency and acceleration ratio, which improves the calculation ability and accuracy of power flow and reliability evaluation of large-scale power systems.