Dynamic safety assessment method based on random forest and extreme learning regression

The invention discloses an online dynamic safety assessment scheme based on random forest and extreme learning regression. The method specifically comprises the following steps: (1) obtaining an electric power system operation data sample by utilizing historical operation data of an electric power system and fault simulation based on an anticipated accident set, constructing a dynamic safety indexand forming an original sample set; (2) obtaining a key variable by utilizing a Gini index and a variable importance score by adopting a feature selection method based on a random forest; (3) training an extreme learning regression machine by using the key variables to obtain a mapping relationship; (4) updating the model by receiving the real-time operation data of the power system from the wide-area measurement system server, thereby completing real-time dynamic safety evaluation of the power system. According to the scheme, rapid and efficient real-time safety assessment is carried out onthe power system, and stab