What is Hardware-in-the-Loop?

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Hardware-in-the-loop testing is a direct method for predicting how physical equipment interacts with control software in real time. Engineers integrate actual hardware components with virtual models to fine-tune complex systems before large-scale manufacturing. This approach helps uncover design flaws early and avoids expensive rework. Project teams appreciate the precision and immediate feedback, which ultimately shorten development cycles.

Many engineering teams often ask what is hardware-in-the -oop and how it aligns with best practices for real-time simulation. HIL offers an advanced way to see how mechanical systems behave under varied operating conditions without constructing full-scale prototypes. Testing procedures become more streamlined and repeatable, helping you reduce cost. Integrating real sensors and actuators into a simulated test framework also ensures data accuracy for thorough analysis.

What Is Hardware-in-the-Loop (HIL) Testing?


Hardware-in the-loop (HIL) testing often prompts a straightforward explanation: it links physical hardware components to a virtual model running in a real-time simulator. This setup evaluates genuine system performance under controlled conditions, which is essential when verifying safety, efficiency, or reliability metrics. Traditional bench testing might reveal certain issues, yet HIL offers deeper visibility because it replicates dynamic events in a repeatable way. Developers use this type of testing to confirm that control signals and power flows are properly managed before field deployment.

The approach typically involves connecting sensors, actuators, or even entire subassemblies to a digital domain designed to mirror operational scenarios. While software simulations alone can guide early development, the presence of tangible hardware adds a layer of authenticity that purely virtual methods cannot match. HIL helps you gather data on the physical responses to varying loads, temperatures, or voltage levels without building a costly test bench. Engineers across many industries, including power electronics and automotive, value HIL for accelerating validation schedules.

How HIL Testing Validates Control Systems




Control systems often exhibit complex interactions between multiple components, making them prone to hidden faults if tested only in simplified conditions. HIL provides a structured domain for testing every control loop with genuine hardware signals, thus capturing real performance data in real time. This reduces ambiguity and offers clarity on how sensors respond and how controllers adjust outputs based on the input conditions. Accurate insights gained from HIL allow engineering teams to refine algorithms and calibrate hardware interfaces more effectively.

For instance, a powertrain control system in an electric vehicle benefits from HIL testing by allowing the battery management unit, drivetrain components, and other modules to work together as they would in normal operation. This integrated approach leads to better alignment between hardware and software, minimizing unexpected failures after mass production begins. Large-scale projects see HIL as an integral strategy because it sets a high standard for performance evaluation at each phase. The result is a stable, well-coordinated system that meets or exceeds compliance requirements.

Main Types of HIL Configurations




Organizations employ various HIL setups to match the specific demands of their development projects. Some solutions focus on micro-level component validation, while others handle entire assemblies or system-level interactions. Different configurations are chosen based on budget, testing frequency, or hardware availability. A well-planned HIL layout significantly boosts reliability and return on investment by ensuring that every part is evaluated under the right conditions.

  • Processor-in-the-Loop (PIL): This setup verifies the functionality of embedded processors by interfacing real processing units with simulated inputs and outputs. Engineers often rely on PIL to highlight timing constraints and confirm if the target processor can handle computational demands. It shows exactly how the application code behaves under real processor conditions.
  • Power Hardware-in-the-Loop (PHIL): This configuration adds actual power components to the loop, such as converters or power amplifiers, allowing teams to assess behavior under load. System stability and safety become clearer because current and voltage waveforms are subjected to genuine electrical effects. PHIL is especially common in microgrid and renewable energy projects that need accurate power flow representation.
  • Electric Motor HIL: This option involves connecting the motor drive hardware to a digital representation of mechanical loads, letting you measure torque responses and other performance metrics. Development teams rely on motor HIL to confirm if speed control algorithms function correctly across a broad range of conditions. This approach identifies mechanical stress points early, which reduces maintenance costs later.
  • Automotive ECU HIL: Automotive engineers often use HIL benches to test electronic control units in real-time conditions without the full vehicle. Signals for sensors like temperature, pressure, or speed are emulated, and the ECU responds as if it were in a running system. This method helps confirm compliance with stringent industry regulations by isolating faults before the final assembly.
  • Mechanical Subassembly HIL: Some organizations test specific mechanical subassemblies, like hydraulic actuators or gearboxes, by coupling them with simulated conditions. The hardware experiences forces and motion that mirror real operation, enabling precise optimization. This configuration highlights how physical wear and tear might develop over time, prompting earlier design modifications.

Selecting the right configuration depends on the nature of your project and the extent of physical component integration required. Some teams combine multiple forms of HIL when working on large systems that span several domains, such as power distribution and vehicle control. Tailoring the approach ensures a balanced combination of scope and detail, yielding meaningful insights that drive better performance. Engineers who recognize these configurations can balance cost and testing depth, accelerating design cycles and production readiness.

Steps to Implement HIL




Effective HIL implementation hinges on a methodical process that aligns real hardware and software models in a stable test domain. Each step addresses potential sources of error and ensures that you gather accurate data for advanced system tuning. Teams reduce cost overruns by mapping out a clear plan before integrating all components. The following core stages help you achieve consistency and thorough validation:

Step 1: Define System Requirements

Clear objectives guide every successful HIL project. Engineers identify the control variables, performance constraints, and hardware specifications upfront. This approach helps you avoid confusion about the signals, data rates, and measurement ranges used during the tests. A structured list of requirements keeps the project focused and lowers the risk of scope creep.

Step 2: Develop Accurate Models

Functional models of the system or subsystem are created in real-time simulation tools, ensuring that the virtual elements mirror the physical domain. Engineers calibrate these models based on known performance benchmarks, verifying that each parameter, such as voltage level or fluid pressure, reflects real-life values. Detailed modeling reduces guesswork in subsequent steps. Verification at this stage lays the groundwork for integrating hardware seamlessly.

Step 3: Integrate Hardware Interfaces

Physical components such as sensors, actuators, or embedded controllers must connect smoothly to the simulator’s I/O channels. Proper cabling, signal conditioning, and data synchronization prevent faulty readings or missed events. This integration process often includes robust checklists to confirm accurate pin assignments and voltage references. Meticulous attention here guarantees that subsequent testing data remains trustworthy.

Step 4: Conduct Preliminary Validation

Initial trials confirm whether the combined hardware and simulation setup behaves as intended under controlled conditions. Engineers might run static load tests or simple operational scenarios to verify signal timing and data acquisition. These smaller evaluations help you fine-tune parameters before running high-fidelity scenarios. Addressing minor issues now can save significant effort once the system is fully operational.

Step 5: Iterate and Optimize

Ongoing refinement is essential after the first validation cycle. Teams examine logs and performance metrics to make incremental improvements, focusing on control algorithms or hardware response times. This iterative approach enhances system reliability by catching subtle design issues early. Each refinement cycle moves the project closer to a validated, production-ready solution.

Challenges in HIL


Implementing HIL may reveal complexities that require technical expertise, careful budgeting, or strong collaboration among multiple departments. These challenges can slow progress if not addressed systematically, yet foresight helps you reduce friction in the process. Some difficulties arise from hardware limitations, while others relate to organizational factors. Identifying these pitfalls early can substantially improve test outcomes.

  • Real-time synchronization difficulties: Maintaining precise timing between hardware signals and the simulator is vital, and any mismatch can compromise data integrity. Engineers often use dedicated hardware interfaces and high-speed protocols to handle this, but setup can be intricate.
  • Limited hardware availability: Some critical components might be scarce or costly, forcing test engineers to share resources with other teams. Efficient scheduling and resource management become necessary to keep the project on track.
  • Model fidelity concerns: High-fidelity simulations require detailed representations of mechanical, electrical, or thermal processes, which can be time-consuming to develop. Oversimplifying these models leads to inaccurate results.
  • Complexity in data interpretation: Large volumes of test data can overwhelm teams if they lack systematic tools for analysis. Well-chosen software solutions and robust data logging practices help you transform raw output into actionable insights.
  • Organizational communication gaps: Coordination between control engineers, hardware specialists, and project managers is crucial for timely decisions. Clear roles and responsibilities reduce misaligned efforts and missed milestones.

Addressing each challenge often involves a blend of technology upgrades, process improvements, and stakeholder alignment. Even advanced teams can encounter setbacks when new components or updated specifications emerge unexpectedly. Practical contingency plans and a willingness to refine initial assumptions keep the program on course. Ultimately, resilience in handling these hurdles benefits the entire development lifecycle.

Key Benefits of Hardware-in-the-Loop




Project leads appreciate the consistent outcomes and measurable gains that HIL testing offers. Speed to market is often boosted by early detection of issues, and budgets are better managed due to fewer last-minute surprises. The flexibility of adding or substituting hardware components enables real-time diagnostics and iterative improvements. A closer look at these benefits highlights why HIL stands out as a practical approach.

  • Enhanced safety testing: Putting actual hardware in controlled test loops avoids risky on-site evaluations. Major hazards or malfunctions are discovered in a safe setting.
  • Reduced development cycles: Iterative feedback from real hardware shortens each testing phase, shrinking the timeline to launch. This efficiency helps you respond more effectively to design changes.
  • Lower overall costs: Early identification of design flaws prevents late-stage rework, which can consume significant resources. Eliminating excessive physical prototypes also conserves budget.
  • Greater confidence in final products: HIL reveals detailed performance data, enabling robust validations of control algorithms and mechanical behaviors. Stakeholders trust outcomes supported by real hardware interactions.
  • Improved collaboration among teams: Engineers, operators, and even financial managers can align on test results, thanks to transparent insights delivered by HIL setups. This alignment drives more coordinated project outcomes.

Organizations that invest in HIL often view it as a strategic asset rather than an isolated testing tool. The capacity to link hardware and software under precise conditions fosters deeper learning about every subsystem. Collaboration around shared data speeds up decisions while ensuring compliance with industry standards. Over time, these benefits compound, resulting in more effective growth.

Trends in HIL for Emerging Technologies


New developments in autonomous systems and renewable energy have placed HIL at the center of advanced product development. Engineers are integrating machine learning algorithms into the simulation loop, allowing predictive insights based on real sensor feedback. This shift elevates test coverage and helps you detect anomalies before they escalate into major failures. The growing need for zero-emission transport solutions also aligns with HIL to refine battery, motor, and charging systems at scale.

Cloud-based platforms now offer remote collaboration features, where distributed teams run large sets of HIL simulations concurrently. This technology accommodates broader test scenarios and speeds up your time to market. Enhanced synergy between hardware and AI-driven analytics refines control system calibration for better efficiency. Many companies view these HIL advancements as opportunities to tap into new revenue streams while minimizing overall risk.

Hardware-in-the-loop fosters robust system development across multiple sectors that demand high reliability and peak performance. The process connects real and virtual elements in a test bed that quickly flags potential issues and paves the way for cost-effective fixes. Engineers and project stakeholders rely on its accurate results to guide essential decisions for product deployment. When executed with a clear plan and scalable approach, HIL stands out as a key driver for quality and efficiency.

Engineers and innovators across industries are turning to real-time simulation to accelerate development, reduce risk, and push the boundaries of what’s possible. At OPAL-RT, we bring decades of expertise and a passion for innovation to deliver the most open, scalable, and high-performance simulation solutions in the industry. From Hardware-in-the-Loop testing to AI-based cloud simulation, our platforms allow you to design, test, and validate with confidence. 

Common Questions About HIL Testing


HIL stands for Hardware-in-the-Loop. It is a technique that integrates physical hardware components into a simulated test framework to ensure accurate testing of complex systems.




Software-in-the-loop (SIL) testing focuses only on virtual models, while HIL adds actual hardware components for deeper insights. The presence of real hardware in HIL captures physical behavior and unique performance factors that SIL alone may overlook.

Budgets remain more stable because defects are discovered early, avoiding last-minute design revisions. Fewer prototypes and rework cycles also lead to substantial savings over the project’s timeline.

Yes. Many platforms support higher power ratings, specialized I/O boards, and dedicated amplifiers to handle industrial demands while maintaining real-time performance.

Engineers want a reliable way to verify electronic control units, powertrains, and safety functions before physical vehicle assembly. HIL exposes software and hardware to real sensor inputs, revealing potential issues under realistic conditions.






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The Founding & Expansion of a Remote Learning Lab from a Long-Trusted Educational Partner

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In early 2020 the world changed considerably, and students, professors, and the learning/teaching communities were among those most affected. Many learning labs were closed worldwide with no notice, interrupting engineering educations. OPAL-RT has a long history and investment of time and cooperation with the educational community, and we were able to marshal our resources in short order to assist.

In Fall 2020, a Virtual Lab pilot program was launched, consisting of OPAL-RT courseware, to help mediate the physical distancing constraints as a first step–but also to help implement the concept of reverse teaching, namely flipped classrooms and labs. In this context, that means applying hands-on practice sessions to the classroom portion of the course, while assigning readings and other classwork to students at home.

It has long been a cornerstone of pedagogical theory that we retain a great deal more of what we actively participate in when learning. The Cone of Learning, below, illustrates these concepts.


In practice, what this looks like is: students do their lab sessions first, in the safety of their own homes, on their own computers, and at their own learning pace—and allow themselves in the process to make mistakes, break virtual fuses, etc. Once they are later in front of the physical test bench, they know exactly what to expect as a result, and how to deal with the hardware they have at hand.

OPAL-RT has been developing courseware since 2014 and incorporating these principles. We started initially with our suite of Power Electronics courseware. Then in 2017, we pooled our efforts to team up with Professor Viarouge and his students from Laval University in Quebec, Canada, to further develop courseware in Electric Motors and Power Systems. It should go without saying that these efforts are win-win for all parties, as we learn more about how students themselves learn, and they are able to avail themselves of world-class real-time interactive simulation concepts in action and solvers/platforms. (This is as opposed to the offline non-interactive simulation they might usually have access to in classrooms and labs.) Over the course of the pilot program, the professors adopted our courseware, adapting it to their needs, and developing new experiments, and sharing that further development with us.

Five universities and colleges have so far participated in our pilot project: Laval University and Collège Montmorency in Quebec, École Navale in France, École supérieure d’ingénieurs de Beyrouth, and the Lebanese University in Lebanon.

For more on participating in the program, please see below.

Testimonials



Prof. Philippe Viarouge

“When we designed this interactive courseware on electric motors in 2017 in collaboration with the OPAL-RT team, we never thought that two years later, we would be using it in the context of distance education imposed by the health crisis. From September to December 2020, a pilot experiment was conducted in the Electrical Machine course of the BSc program in Electrical Engineering at Laval University in Quebec City with 33 students. Everyone had access at home to their personal virtual laboratory to perform their laboratory experiments and their own investigations for the assimilation of learning.

I systematically used the virtual laboratory during the Tutorials of synchronous virtual classes in a reverse teaching approach. Several exercises in the written exams were based on analyzing performance and identifying specific training operating points from images of the adjustment and instrumentation panels. Finally, 33 one-hour individual laboratory examinations were carried out entirely remotely by videoconference with the teacher. On the occasion of the success of this experience which elicited unanimous comments among the students, I discovered with a certain enthusiasm in this versatile educational tool the possibilities of use and assimilation that I didn’t expect during its initial conception. This generic concept constitutes a powerful tool at all levels of training and in many fields.”



Prof. Flavia Khatounian

“The obvious advantage of this courseware is the concrete comparison with the lab experiments that the students would usually carry out in the laboratory and on many levels: protection against excessive current draws, variable and reduced voltage power supplies, different assemblies achievable by simple changes of connections… This gives a certain autonomy to the students who thus prepare themselves by being more at ease since handling errors are detected and interrupted without damaging any equipment.

Finally, given that the exercises are provided with a lot of details and explanations as well as with the course concepts covered in each simulation, this gives a relatively complete overview for my 22 students.”







Prof. Jean-Frédéric Charpentier

“Even if we give the course face to face at the naval school, these courseware represent an excellent advantage because they constitute the intermediate step between the theoretical course and laboratory sessions on real benches. In addition, our 80 students are familiar with the concept of simulation, using boat navigation simulators. Why not extend this same concept to electric machine laboratories? Finally, with the courseware, we can push the systems to their limits and perform experiments that we would not do on real machines.”






Prof. Rita Mbayed 

“Virtual labs have been of great importance to us in this time of pandemic where access to the real lab has not been possible. The experiments are clear, easy to handle, well-focused, and fit perfectly with the goals of the Electric Machine Course. With that, it is obvious that this virtual lab will have its place in my course from now on.”











Mr. Sylvain Brisebois

“The OPAL-RT solution will be used to add a dynamic component during the theoretical course sessions. Students will also be invited to install the courseware on their personal computer in order to carry out laboratory preparations. This is a solution that comes just in time in the current context of physical distancing but definitely will be used even later on too.”







If you are interested in participating in the extension and expansion of this pilot project, please communicate with Dr. Danielle Nasrallah (danielle.nasrallah@opal-rt.com), manager of the pilot project and developer of much of OPAL-RT’s courseware. 


Danielle Sami Nasrallah received an Engineer’s diploma in electromechanical engineering and a Diplôme d’Études Approfondies in electrical engineering from École supérieure d’ingénieurs de Beyrouth (ÉSIB), Beirut, Lebanon in 2000 and 2002, respectively, and a Ph. D. degree in Robotics Modelling and Control from McGill University, Montreal, QC, Canada, in 2006. During her Ph. D. studies she worked on a part-time basis at Robotics Design as a control and robotics engineer. She moved to Meta Vision Systems in 2006-2007 as a control and applications engineer. In 2008 she joined the electrical department of the Royal Military College of Kingston as an assistant professor and, in 2009, she was a visiting assistant professor at the American University of Beirut. From 2010 to 2014, she worked as a consultant in control and systems engineering. In 2014 she joined OPAL-RT Technologies where she is presently a technical lead in control and intelligent mobility.  She retained links with academia as she lectures in Robotics and Control at both Concordia and McGill Universities.

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The ‘Digital Twin’ in Hardware in the Loop (HiL) Simulation: A Conceptual Primer

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Michael Grieves first uttered the term ‘digital twin’ in 2003, and since then much ink and many pixels have been spilled over it. We’ll keep it relatively brief. While ‘digital twin’ can mean many things to many people, it has a more restricted set of understandings when we speak in the context of real-time simulation and/or Hardware in the Loop (HiL) testing.

To begin to explain this closer-cropped meaning, let’s start by thinking of a mirrored copy of a hard drive. It’s a periodically-updated copy of a hard drive in current use, intended to be redundant if/when required. The contents of the mirrored drive can be validated as a duplicate of the source drive at any given time. So it’s a four-dimensional protective mechanism (length/width of data, bit depth, plus the dimension of duration, or time) of a hard drive. Yet already this metaphor lacks the complexity required to outline everything we need to speak about here adequately.

The Iron Bird in the Digital Era

An additional metaphor may serve us better here. In the aircraft design and engineering cycle, which includes MEA (More Electrical Aircraft), a concept exists known as the ‘iron bird’—an integration test rig. All the systems and subsystems of an aircraft are assembled, laid out on the floor of a hangar, say, so that the entire plane is, in essence, operational, except the chassis itself–but it is not physically in the air.

Now to clarify: the iron bird, at the time of its inception in circa 1985, was a hybrid physical/simulated plane during a time where planes evolved exponentially more numerous and more complex systems. (Reasons for more in-air tests as these ‘systems of systems’ grew not being more common should be obvious: massive expense, countless permutations and combinations, huge development times, loss of life, etc.)

All the constituent pieces of this assemblage are validated, and are receiving live stimuli and issuing reactions and outputs as though the plane itself were flying–but interactions with engines, landing apparatus, wing flaps, etc. may or may not be virtual, depending on the phase of Verification and Validation (V & V). The iron bird testing phase must be as good (validated, accurate, entirely reproducible) as ‘real life’ i.e., as a real-world flight—there is very little to no ‘wiggle room’ in aviation–or this testing phase and concept serves no purpose.

The use of iron birds for aerospace V&V, where physical components are partly replaced with digital/virtual parts and through using real-time simulation—again, depending on the V&V phase—allows aircraft manufacturers and their equipment developers to save vast fortunes of money on expensive prototyping. Though of course, at the end of the day, virtual models need some more extensively validated dynamics and response validation before they are ready to jet you off to your next sunspot vacation.

A Digital Twin: A Great Deal More Than Just a Simulator

Now, if we add some other elements to the duplication/redundancy notion (see: the mirrored copy of the hard drive), and add some levels of complexity, interchangeability, and communications (see: the iron bird), the functioning of what we currently mean by ‘digital twin’ in the context of real-time simulation (or HIL) begins to be clearer.

If we were to break it down functionally by what it does, is able to do, and at what it conceptually excels in our current reading:

  • The digital twin can read data to/from its physically operating counterpart and report on itself via its hardware/software surroundings to an overseeing entity—for maintenance, for logging, for reporting, for control, etc.
    • Meaning: the digital twin—itself virtual–has a bridge of connectivity with the ‘real-world’
  • The digital twin has a profile of its own in the form of a dynamic model and related parameters, and it is  adjusting itself, in close comparison, with its physical counterpart in real-time or ‘near real-time’
    • Thus, it is self-adaptive
  • The digital twin is able to describe its inner components’ interactions in much greater detail than its physical counterpart can, since the latter has a limited amount of sensoring
    • Thus, it can act as a one-step removed, in-depth observer
  • The digital twin can respond to the same stimuli as its physical counterpart and help identify abnormal operations, malfunctions and identify the source of the problem
    • Thus, it is a situationally-aware and decision-supporting tool
  • The digital twin can respond to synthetic stimuli and help in obtaining what-if scenario analysis that cannot be analysed at will through its physical counterpart due to practical concerns. In this way, it will differ from a simulation (or real-time simulation) as it is a validated dynamic replica of its physical counterpart
    • Thus, it is extremely powerful as a predictive tool, and can be used additionally as a design and planning tool
The image above describes how a power system digital twin might interact with its physical counterpart through advanced analytics, dynamic and steady-state data management, automation, and system operators. This is a key technology that will help ensure the secure and reliable operation of future power systems throughout their life cycles, as we are now seeing the increased penetration of renewable energy and decreased inertia. The digital twin is described as multiple instantiations, each with their own purpose, architecture, and mathematical model representation. Real-time simulation technology is definitely a key enabler for many applications, including the acceleration of predictive simulations, and the digital twin may exist as a model of models, in phasor dynamic, electromagnetic transient, machine learning models or other forms depending on the purpose, or Digital Twin service.

Digital Twins: The Current State of Affairs

As a real-time simulation afficionado, the potential uses should be becoming clear to you by this point, but to give some genericized examples of current uses:

  • An electrical car manufacturer can accumulate vast volumes of data on its vehicles during their operation. The data is pivot-table ready to the extent that it can be logged, viewed, and analyzed by driver type profile, geographical location of car, and many other slants or viewpoints on the data that may unearth valuable and/or useful results. (One of ‘big data’s’ chief strengths is the myriad of angles from which it can be read, each new angle potentially offering new insight.) Through this, the manufacturer can send notifications to the local garage, or to the end user, when a Battery Management System has unexpected behaviour, for example, or for engine part replacement.
  • An electric utility can learn about usage/consumption patterns through this combination of logging/reporting, AI, and vast amounts of data to automate what is now called Demand Response, through adjusting residential boilers and heaters to ensure sufficient spinning reserve or stable operation.
  • In the near future, predictive maintenance where—before failure has even occurred in a consumer good—a potential future breakdown can be predicted, all the possible routes towards a positive outcome can be analyzed, and action can be taken, before the user is even aware anything untoward has happened.

Into the Great, Wide-Open Future

We’re on the cusp of this particular combination of present and future-looking enhancements that can be applied across many categories for the benefit of design, prototyping, regular use and maintenance/replacement. The clustering of various recent phenomena has made this approach and all of its implicated uses possible: big data, AI, 5G and faster networks, cloud computing, and the Internet of Things, among others.

How will ‘digital twins’ help in the next 50 years and onwards for power grid operation? They’ll help stabilize our power systems through diagnosis, monitoring, experience, and predict through ‘what if’ scenarios. They are, in a way, digital time travel: going back in time to examine histories of aggregated data; going forward to predict outcomes. They’ll help us learn to operate our power grids better and more safely; and they’ll provide many times more and better test coverage for upgrades and improvements.

As they do all of this quietly and reliably, they’ll also provide better, more numerous, and more detailed data sets, and thus improve the training and output of AI immeasurably. They’ll also allow us to explore edge/corner cases we might never have thought of even simulating, based on their physical twin’s data. They will help in finding countermeasures to prevent wide area failures and to decrease grid downtime. They’ll improve autonomous system operation, and they’ll help optimize our simulation scenario selection through model-trained AI.

‘Digital twin’ is a concept where the language has been used fairly loosely thus far–yet combinations of the advances made possible through this thinking promise exciting and inevitable advances for real-time simulation. It’s not a matter of if this concept and its associated wholesale improvements and nuances are leveraged, but when and how.

Learn more

Watch the recorded RT20 Panel Discussion on Digital Twin to Increase Resilience of Power Grid

User Explainer | Remote Use of OPAL-RT Simulators & Platforms

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Despite the current global fight against COVID-19, OPAL-RT continues to work hard in serving its customers with the best operational efficiency possible. With the amazing collaboration of our own employees—and thanks to our stellar IT department—we’ve succeeded in requiring less than 48 hours to optimize our own remote work.

All of our users—Academic/Research, Industrial—can achieve the same if they’d like, thanks to the combined use of:

  • Virtual Private Network (VPN)
  • Remote Desktop Protocol (RDP)
  • Collaborative and web meeting platforms (such as Microsoft TEAMS) and
  • Use of our cloud-based file libraries (such as Sharepoint Online)

OPAL-RT simulators are already “remote-work friendly” by design, thanks to the host/target architecture. This enables users to run simulations from a local host PC, controlling the simulation and acquiring simulation results/data on the OPAL-RT Target, which are in turn both connected to their lab or office network. The user can remotely control this host computer using their own PC, in the comfort and safety of their home….

Recommended Best Practices for the Remote Use of OPAL-RT Simulators

  • Users can access an enterprise RDP server/PC directly from the internet by allowing RDP traffic to/from the router—but this is not considered optimally cyber-secure.
    • Make sure you take all precautions and safety measures before applying such access rights
  • Have your IT department setup a VPN and define an access procedure
  • Using it, connect to your VPN
  • Access the simulation Host computer through RDP, using the host station name (DEVICE NAME)
  • Make sure the RDP server settings are set up properly to avoid one user disconnecting another while using RDP on the same device
    • There are also usage time-out settings/policies your company may want to revise with your IT department
    • As a best practice, you may want to keep an Excel LOG file in which users can reserve the use of the host PC using RDP
  • When you are finished using the remote host PC, do not forget to close all software before logging off—this is particularly important if you use a limited number of floating licences

Accelerate Your Simulations Using HYPERSIM® on Demand

HYPERSIM on Demand is the remote/off-site/cloud version of one of our leading platforms: HYPERSIM.

OPAL-RT presents HYPERSIM On Demand, a simulation platform to accelerate the prototyping, development and testing of power system equipment. The solution that enables parallel execution of simulation tests on multiple cloud simulators also offers staggering performance gains over standard EMT simulation software.

  • Use only what you need, when you need it, with commitment-free pay-as-you-go pricing.
  • Run models and scenarios on your virtual simulators, then use your own machines for the rest.
  • Extend your model simulation abilities by as much as required, for as long as required.

Learn more about HYPERSIM On Demand >