The invention relates to a power
system transient stability evaluation method based on a
deep learning technology. Firstly, a
time domain simulation method is used to generate a sample set {x<0>, y<0>}; characteristic variable vectors are then extracted according to the sample set, and a
training set {x<1>, y<0>} is formed, wherein the
training set is the characteristic variable vector set; training parameters are determined, a stacked automatic
encoder is trained based on the
training set, characteristic extraction is carried out on the training set to generate a calculation set {x<2>, y<0>};and finally, based on the calculation set, classification model training is carried out on a
convolution neural network, and a power
system transient stability evaluation model is formed. The stackedautomatic
encoder is used to carry out layer-by-layer characteristic extraction on the characteristic variable vectors, a
hidden data mode is mined, high-order characteristics more facilitating transient stability evaluation are formed, the
convolution neural network is further used to build a stable classification model, the evaluation performance of the model is thus ensured, the misjudgement rate of unstable samples can be reduced,
noise interference in a
wide area measurement
system of the power system can be effectively overcome, and an important significance is provided for online safeand stable evaluation on the power system.