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A Digital Substation in-the- Loop (DSiL) at INESC TEC

INESC TEC

Academia, Energy

07 / 17 / 2026

A Digital Substation in-the- Loop (DSiL) at INESC TEC

The Customer

The Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) is a leading R&D institution in Portugal with international reach, focused on scientific research and technological development, technology transfer, advanced consulting and training, and pre-incubation of new technology-based companies. Its Centre for Power and Energy Systems (CPES) works in power and energy systems, including the large-scale integration of Distributed Energy Resources.

The Challenges

Realistic Interoperability: Validate multi-vendor interoperability under realistic network timing and load conditions.

  • Deterministic Performance Assessment: Assess system performance with hardware-timestamped evidence.
  • Advanced Protection & Control: Develop and validate advanced protection and control logic.
  • Pre-Deployment Validation: Thoroughly validate and de-risk new applications before deployment.

The OPAL-RT Solution

In response to these challenges, INESC TEC is developing the Digital Substation in-the-Loop (DSiL) platform using OPAL-RT’s solution as the technological provider.

  • Real-Time Digital Substation Platform: The DSiL platform uses an OP5707XG real-time simulator running EMT models with a 125 μs time step, supported by RT-LAB and HYPERSIM.
  • IEC 61850 & Multi-Vendor Integration: Physical and virtual IEDs are synchronized through IEEE 1588 PTP, with IEC 61850 GOOSE, Sampled Values, and MMS communications.
  • Repeatable HIL Validation: The platform combines physical hardware-in-the-loop, analog front-end amplifiers, automated campaigns, and structured test data for repeatable PAC validation.

The Results

  • Large-Scale Validation: The Adaptive Protection Scheme (APS) was evaluated across 1,210 operating scenarios and more than 60,000 fault scenarios.
  • Improved Protection Performance: The APS reduced the failure rate from more than 35% to 0.67% and achieved 99.9% fault clearance across the evaluated scenarios.
  • High Classification Accuracy: The machine-learning classifier achieved 99.8% accuracy across 1,210 operational scenarios.
  • Fast Real-Time Response: HIL validation demonstrated an average decision time of approximately 0.6 ms, with the overall classification and setting activation process completed in under 1 ms.

 

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