Transient Stability Assessment of Auto Encoder Considering Frequency Shift of Inertia Center

Aiming at the problem that the traditional deep learning method does not consider the physical characteristics of power system when evaluating power system transient stability, a power system transient stability evaluation method considering the frequency offset of the system inertia center is proposed. By calculating the frequency offset of the inertia center after the power system faults, the samples are classified and trained with the stacked sparse auto encoder. When the grid structure of the system changes, the method of transfer component analysis combined with the inertia center frequency offset is used to update the classifier. Simulation results on the New England 10 machine 39 bus system show that the proposed method has higher accuracy and stronger generalization performance than the traditional deep learning method and the transfer learning method. The proposed method still achieves good results when some synchronous vector measurement units are missing or there is noise in the data. © 2022, Power System Technology Press. All right reserved.