The invention relates to a stability evaluation method and device of a power
system,
computer equipment, a computer readable storage medium and a
computer program product. Comprising the following steps: for any iteration training, acquiring data from a stability
data set of a power
system, and performing
feature extraction on steady-state operation data, fault process data and
power grid topology data to obtain fusion
latent variable features; inputting the fused
latent variable features into a stability evaluation network to obtain a
stability result and a margin result; determining
stability loss and margin loss based on the
stability result, the stability true value, the margin result and the margin true value; determining a stability weight and a margin weight; based on the
stability loss, the margin loss, the KL
divergence regular term, the stability weight and the margin weight, determining the total loss of the model, further judging whether a preset condition is met or not, and if not, continuing iterative training; if yes, the training is completed. According to the method, stability prediction and margin prediction can be collaboratively optimized.