The Generation Method of Power Flow Simulation Samples Based on Power Flow Cut Adjustment

The operation of large-scale power grid dispatching is complex, which requires deep learning to study its characteristics, while the assessment of the potential risk of power grid dispatching operation requires a large number of samples. At present, the problem is the lack of samples. In this paper, a new generation method of power grid simulation samples based on power flow cut adjustment is proposed to solve the problem of insufficient power grid simulation samples. Using this method, a large number of samples can be generated quickly, which provides more effective samples for the application of data-driven method in power system research.