In recent years, Electric Power Systems have been operated closer to their operating limits. Under these conditions, certain unforeseen disturbances can produce cascading events that eventually lead the system to collapse. It is necessary to ensure that such disturbances do not affect the quality and continuity of the electricity service. In this sense, the need arises to develop mathematical models and practical tools that allow the design of a Self-Healing Grid as part of the concept of a Smart Grid, capable of carrying out reconfiguration functions and wide area control in real time, with the aim of reducing the risk of system collapse.
The dynamic security assessment (DSA) constitutes a fundamental part of this objective since it allows to decide and appropriately coordinate the most appropriate preventive or corrective control actions for the system. In this context, the National Electricity Operator of Ecuador, CENACE, has developed several methodologies aimed at performing DSA, which have been structured in MATLAB-SIMULINK, enhancing its functionality to implement Machine Learning algorithms, as well as its potential to interact with the OPAL-RT Real Time Digital Simulator.
This presentation shows a summary of the methodologies developed, the results obtained and outlines the future plans, based on the great potential of the analysis tool, for its implementation within the real-time operation processes.