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Power System Simulator based on Physics-Informed Neural Networks

Project description

Revolutionary neural network-based power system dynamics simulator

In recent decades, power system dynamics simulators have become increasingly important, with conventional approximations quickly becoming unfit for the task and requiring significantly longer time spans to provide accurate results due to more complex and sensitive circuitry. Unfortunately, faults can cause phenomena to propagate faster. Electromagnetic transient (EMT) simulation can accurately capture them, but has become slower and more computationally intensive in recent years, as it must operate in the midst of millions of possible critical scenarios. The ERC-funded PINNSim project aims to develop a power system dynamics simulator based on neural networks that is highly modular, scalable, and can be up to 100 times faster than current commercial alternatives, dramatically reducing time requirements for stability assessments.

Objective

This proposal puts forward a first-of-its-kind power system dynamics simulator based on neural networks: PINNSim. PINNSim aims to deliver a highly modular and scalable tool that can be up to 100x faster than any commercial power system simulator. This means that PINNSim can perform a power system stability assessment in 30 minutes instead of 2 days.

We witness a dramatic shift from few large scale generators to millions of converter-based resources, such as solar, wind, batteries, and electric vehicles. Capturing the dynamic behavior of such a system is no longer possible with the conventional approximations. In case of fault, phenomena propagate much faster and an ElectroMagnetic Transient (EMT) simulation is necessary to accurately capture them. However, EMT is slow and computationally intensive. Uncertainty from solar and wind and the increased number of components requires the assessment of millions of critical 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.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

(opens in new window) ERC-2025-POC

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Host institution

DANMARKS TEKNISKE UNIVERSITET
Net EU contribution
€ 134 000,00
Address
ANKER ENGELUNDS VEJ 101
2800 Kongens Lyngby
Denmark
Region
Danmark Hovedstaden Københavns omegn
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Beneficiaries (2)