Common methodologies

in Model Based Systems Engineering

The new grid is all about software.


A smarter grid requires smarter testing.


Software-driven power electronics converters increasingly influence modern power systems. They make the power systems smarter, but also more dynamic and complex. The biggest challenge of grid digitalization is to ensure that all grid components work together seamlessly. In all possible real-life scenarios.

New testing requirements


Today, we can say with a high level of confidence that we have a good understanding of how the physical layer of the network behaves. With all the knowledge and time spent developing mathematical representations of physical components, we know we can simulate passive components, cables, machines and even semiconductors with optimal fidelity to evaluate their behavior under certain operating conditions.

On the other hand, the control (cyber) layer is fast-evolving and unpredictable, which makes it extremely difficult to simulate.

As the behavior of smart network devices dominating the power systems today vastly depends on control, we had to develop new methods and tools that will enable us to validate control behavior in as many operating points as possible. Testing control behavior, especially under faulty conditions, is next to impossible in the presence of power.

Balancing cost and fidelity


Controller HIL enables both.

Our abilities to effectively test the behavior of network components we are adding to the grid got additional challenged with penetration of control software.

Historically, we developed various methodologies to help us increase our testing efficiency and minimize the time we spend in the power labs. On one side, offline simulation meant simulating control, which is a task easier said than done. With every attempt to simplify control for testing purposes we ended up loosing testing fidelity. On the other side, testing software with power (regardless if it was full power, or scaled down like with Power HIL) was time-taking and expensive.

With the breakthroughs in computing technologies, we came to the point where we were able to digitalize (emulate) power layer, while keeping the control under test completely intact. This concept is today recognized as Controller HIL (C-HIL).

New way of testing converter control


Validate behavior in all imaginable operating conditions.

With Controller HIL (C-HIL) the power layer is emulated on a real-time platform with high-fidelity mathematical models while the controller remains intact. This way C-HIL has the fidelity virtually identical to full power tests, but at the fraction of the cost.

The elimination of power allows for test automation and validation of controller hardware, firmware, and software in thousands of operating points. Most importantly testing for faults!

Test equipment requirements


Your test equipment is as good as the most technically challenging part of the system it can test.

Technical requirements for test equipment grow as the product is reaching market maturity. If there is a requirement we cannot effectively test with our model-based toolset available in-house, back to the lab we go.

To ensure we are getting the most out of our investment, we need to make sure our test equipment satisfies at least minimal requirements for C-HIL testing. For power electronics converters, we need:

  • high-fidelity and real-time ready models of semiconductors to emulate switching behavior,
  • execution of high-fidelity models with a very small simulation time-step (typically below 1 μs, and as low as 200 ns),
  • ultra-low latency Analog IO, to provide accurate signal feedback to controller inputs (analog outputs should be updated at the same rate as simulation),
  • ultra-fast Digital IO, to support high switching frequencies (typically below 10 ns, and as fast as 3.5 ns, for converters switching above 100 kHz).

With minimal requirements satisfied, we can start exploring additional benefits that different HIL concepts or integrated HIL vendors can provide for particular applications, like expert support, test automation capabilities, test driven development capacity, configuration management, etc.