As the number of large wind and solar connections increases, any potential interaction, due to the differences in their converter control system, will be an important consideration during planning and design studies. 

It will be increasingly important to understand the impact of any new connection in terms of unacceptable oscillatory behaviour considering the possible sources of uncertainty (e.g., forecast errors, parameter errors) and variability (e.g., wind speed) that can affect the network condition.

This project will explore, develop, and test a combination of novel frequency domain methodologies and machine learning techniques to identify potential system operating conditions which can lead to Sub-Synchronous Oscillations (SSOs) and implement an automated control interaction studies framework.

Benefits

The project will enable a significantly improved characterisation and management of complex dynamics of the evolving GB electricity transmission system. The project will address and enhance computation time for EMT type studies and the ability to scan a wider pool of scenarios. A significant impediment to an exhaustive search of potential scenarios of concern regarding the SSO phenomenon is the inherent computational burden of EMT simulations. 

In addition to this, the huge volume of data generated from the scenarios is difficult to process, navigate and analyse without an automated framework. This project will precisely provide solutions to these challenges by using advanced frequency-domain techniques. Another big challenge in root cause analysis of SSO events is proprietary controllers' 'black box' model. The project would look into techniques such as 'impedance participation factors' to represent such models as 'grey box', a technique recently developed in scientific research.

This project will build on the learnings that have already been gathered from other innovation projects in the area of EMT modelling and taking this forward into improved and automated analysis capabilities. This will allow for many scenarios and uncertainties to be captured while performing EMT types of analysis.