Identification of Power Angle Instability and Transient Voltage Instability Based on Deep Learning

With the continuous expansion of the scale of modern power grid and the increasingly complex modes of operation, the issues of power system stability become prominent. Transient stability and dynamic stability have always been serious problems that threaten the safe and stable operation of the power grid, and it is very difficult to distinguish between transient power angle stability and transient voltage stability in actual analysis. In recent years, the third generation of artificial intelligence technology represented by deep learning has developed rapidly, and it has achieved remarkable results in the fields of classification and aggregation. It provides technical support for identifying the type of instability and discriminating power oscillations of different mechanisms. This paper proposes a method for identifying the modes of power angle instability and voltage instability based on Deep Belief Network. By constructing the deep learning model, it is applied to feature extraction of power angle stability and voltage stability.