SOURCE-LINKED INTELLIGENCE

Power System Simulator based on Physics-Informed Neural Networks

CORDIS · observation · Publication date unknown

tical scenarios so that power system operators eliminate blackout risk, and manufacturers design appropriate controllers for the safe operation of their equipment. PINNSim leverages physics-informed machine learning and GPU acceleration to model power system components. Its key novelty lies in the method it interconnects individual neural networks to form a “system of neural networks” that performs simulations at much higher speeds. Through this funding, we aim to scale PINNSim to handle larger, more complex power systems and incorporate a user interface, creating a user-friendly simulator capable of addressing a wide range of power system operation challenges. Our goal is to create a tool that will disrupt the power system simulation software landscape. We envision a library of component models based on neural networks, which can then be used in a plug’n’play fashion in PINNSim, similar to conventional simulators. If successful, PINNSim can naturally extend to almost any non-linear dynamic system such as robotics and biological systems. Physics-Informed Neural Networks, Physics-In

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR