With the ongoing development of modern power systems

short-circuit current limit violations have become increasingly prominent

and single current-limiting measures are no longer sufficient to ensure safe system operation. To address this issue

this paper proposes a mechanism-data-driven power system optimal operation method based on short-circuit current constraint learning. First

to cope with complex operation modes in power systems

a set of combined current-limiting measures

including line switching

busbar sectionalizing

and unit start/stop

is proposed. Second

to overcome the limited ability of multi layer perceptron (MLP) models in learning topology variations

a topology feature enhancement method based on One-hot encoding is proposed to improve the model’s adaptability to current-limiting measures. Third

the concept of short-circuit current safety margin is introduced to quantify the degree of violation of short-circuit current constraints under different current limiting measures. The big-M method is then employed to process the forward propagation formula of the MLP

thereby establishing data-driven short-circuit current constraints. Finally

with the objective of minimizing the total cost of unit operation and network topology adjustments

and considering grid operating constraints

N-1 security constraints

and short-circuit current constraints

a mechanical-data-driven power system optimal operation model based on short-circuit current constraint learning is established. Case studies verify the effectiveness of the proposed model.