The project seeks, as a proof of concept, to use machine learning to create a surrogate model from a” black box” model of an AC/DC converter. The black box model and the surrogate are to be of the type used in PSCAD, a type of electromagnetic transient (EMT) simulation software. 
The aim is to create a surrogate model that has sufficient accuracy that it can be used by ESO in stability studies.  
The surrogate model must be available as source code that can be recompiled so it can work on all future software systems, and it must be able to run at different time steps to ensure compatibility with other converter models (whether surrogate or black box). 

Benefits

• Avoids issues with incompatible black box models.  
• A single surrogate model could represent many diverse converters connected to the same substation.  
• At present converter manufacturers do not maintain models long-term, so models cease to work as the simulation environment is updated. In contrast the source code for surrogate models is available, so they can simply be re-compiled to work with the latest software. 
• Potential for faster execution of studies, as surrogates avoid the “slowest ship in the convoy” effect. 
• Potential for simpler, standardised, converter models without software and legal complexities. This would make building EMT grid models much easier.   
• Avoidance of “lock-in” with particular EMT modelling software – surrogate models can be re-compiled to work with any new software.