Machine Learning for the Assessment of Transient Stability of Power Systems

It is becoming increasingly difficult to conduct standard stability studies on modern power systems due to the growing use of power electronic devices. Power electronic converters, regardless of their power or voltage, must contend with a rising challenge known as electromagnetic transient stability. For power system stability analysis and control, transient stability assessment (TSA) is essential. On the basis of numerical integration of the dynamics equations for an EPS under consideration at a particular disturbance, a method for assessing transient stability reserves is described. In order to improve the transient stability of hybrid AC/DC power systems, a multi-HVDC emergency coordinated modulating method has been suggested in this study. Multi-DC lines are used to build and test the AC/DC hybrid power system. Machine learning models that forecast power system transient stability are more reliable, versatile, and robust if they incorporate knowledge of power system dynamics into their data gathering process. Fault location and how disturbance effects spread over the network are critical for determining unstable regions of operation, thus this determination is made using domain knowledge. That’s why traditional machine learning models may now be used in an instance transfer learning framework thanks to a whole new data gathering technique. Therefore, the suggested solution does not rely primarily on the collected data, but also incorporates system insights.