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Can you show the fault commands use in your run?

"Network not converged" messages typically appear during 'on-fault' simulation periods. After the fault clears,the network should converged unless a collapse condition is found.

Could it be that the test fault is too severe, reaching and remaining in collapse as soon the fault is applied?

Also consider that the problem occurs after swapping generator models. Maybe the new models have invalid parameters (untuned).

~~Can you show the fault commands use in your run?~~

"Network not converged" messages typically appear during 'on-fault' simulation periods. After the fault clears,the network should converged unless a collapse condition is found.

~~Could it be that the test fault is too severe, reaching and remaining in collapse as soon the fault is applied?~~

Also consider that the problem occurs after The swapping ~~generator models. Maybe the new models have~~ of models with invalid parameters ~~(untuned).~~

(untuned) led to NAN results in your second run. A first step in 'tuning' model parameters is to review the log file for the run, the "initial condition suspect" table. PSSe will list models that require attention. Also, open PSSe and run "do_simulation.py", with everything commented after reading the dyr data and check in the dynamic section those models in yellow. Double-click a model in yellow to see its suspect parameters in yellow. Open PSSe documents, Program Application Guide, Vol2, Chap 26 "Data Verification, 26.5 Parameter Ranges for Activity DOCU and then per each model 'in yellow' check that its parameter in yellow fall in the recommended range. Once there are no models in yellow, chances are that a run will behave much better than before.

Model tuning might require optimizing model parameters for a specific dynamic response, like frequency response from governor models in a power unbalance condition or PSS tuning due to power oscillation. Third party tools required....

BTW, add the following code before calling STRT_2,

# lower the acceleration factor (0.8) or the step size (0.004) for non-convergence scenarios outside a fault
psspy.dynamics_solution_param_2([99,_i,_i,_i,_i,_i,_i,_i],
                                [0.8,_f,0.004, 0.016,_f,_f,0.8,_f])
psspy.set_relang(1,38,r"""1""")       #to help with plotting of angles

Lastly, check the forum on "Model Tuning" for related information.