
Real-time electromagnetic transient (EMT) simulation for complex power systems and AI-driven grid optimization.
HYPERSIM, developed by OPAL-RT, is the industry-standard real-time digital simulator designed for the modeling and simulation of large-scale power systems and complex power electronics. In the 2026 market landscape, HYPERSIM has evolved into a critical nexus for AI-driven grid modernization, providing the high-fidelity synthetic data required to train neural networks for predictive maintenance and autonomous grid rebalancing. Its architecture leverages multi-core parallel processing and FPGA-based hardware-in-the-loop (HIL) capabilities to achieve simulation time steps in the sub-microsecond range. This enables utilities and manufacturers to test control systems under extreme transients that are impossible to replicate in physical environments. The software's openness, specifically its Python-based API, allows data scientists to integrate machine learning frameworks directly into the simulation loop, facilitating the development of 'self-healing' grids and high-efficiency HVDC systems. Positioned as a mission-critical tool for the energy transition, HYPERSIM bridges the gap between traditional electrical engineering and advanced AI solutions architecture.
A proprietary solver that allows the simulation of power electronics with frequencies up to 100kHz on standard FPGAs.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
An integrated environment for running thousands of contingency scenarios without human intervention.
Advanced frequency-dependent modeling for transmission lines and cables.
Simulates network delays and packet loss within the control loop to test vulnerability to cyber-attacks.
A hybrid solver that switches between EMT and Phasor domain for large-scale stability studies.
Native hooks for PyTorch and TensorFlow to ingest real-time simulation data for online model training.
Run different parts of the same system at different time steps (e.g., slow grid, fast converters).
Ensuring the control logic of High Voltage Direct Current links can handle sub-cycle faults.
Registry Updated:2/7/2026
Generating training data for ML algorithms to identify fault locations in underground cables.
Testing the transition from grid-tied to islanded mode in renewable-heavy systems.