The random

intermittent and weak inertia characteristics of renewable energy generation have led to a prominent problem in the frequency safety of high-rate renewable energy power systems. The use of time-domain simulation for frequency safety assessment has the disadvantages of large amount of calculation and long time. It is difficult to meet the rapid assessment requirement of frequency safety under the “combined explosion” of multiple complex uncertainties. In order to realize online analysis and prediction of frequency safety

a method based on multi-layer extreme learning machine(ML-ELM)is applied. The non-linear mapping relationship between the input layer and the hidden layer is built by the deep structure theory and in the layer-wise unsupervised training

automatic encoder algorithms and regularization coefficients are introduced to optimize the weight matrix between the input layer and the hidden layer

so that the ML-ELM can effectively represent complex functions and improve predictive accuracy and generalization ability. Case studies of the IEEE RTS-79 system demonstrate the rapidity

high accuracy and well generalization ability of the proposed method.