Recognition and Mitigation of Electromagnetic Transients
Photovoltaic solar panels and recent wind turbines do not have the rotating mass that conventional energy generation has, which generators and system operators have long used to modulate the frequency, current, and voltage of power entering and transiting the energy grid. As a result, when such electromagnetic transients (EMTs) occur, solar- and wind-farm operators have a problem, in that their energy feed goes out of spec from the system operator’s perspective and they are directed to stop sending their energy to the grid. This means their generated power and the resulting revenue is effectively lost unless they have substantial storage capacity, which itself costs money. Solar- and wind-farm operators need to recognize EMTs as they are about to happen and mitigate their effects before their energy feed goes out of spec.
Simulating EMTs with conventional high-performance computing methods has made great progress in the last several years, but it consumes so much computing and time that it is not appropriate for the near-real-time context of solar and wind farms, where a response in milliseconds or even microseconds is needed. XFR sees an opportunity to deliver XAI by using generative AI (GAI) to scale conventional simulation results to larger farms and then processing those results with advanced math so they can be validated, initially by humans but eventually in an automated way.