Leveraging Simcenter HyperStudy & PSIM Part 2: From Integration to Optimization

Check out part 2 in this series, where we take the coupled PSIM and HyperStudy model from DOE to optimization.

HyperStudy design of experiments results with parallel coordinates and a Pareto chart
On This PageOptimization Setup
  1. Optimization Setup
  2. Running the Optimization
  3. Optimization Post-Processing

In a previous blog post and video, we discussed how to successfully integrate Simcenter PSIM, the frontrunning tool of circuit design and analysis, into Simcenter HyperStudy, an industry leader in design exploration and optimization. One of the primary benefits of HyperStudy is providing the ability for users to simulate and compare a wide range of studies for a given design, all without the need to manually iterate. With these tools working in tandem, it becomes possible for engineers to rapidly simulate, analyze, and optimize power electronics, motor drives, and circuit schematics in a range of other applications. Let’s take a closer look at some of the specific options available once the connection is established between these software solutions.

Buck converter schematic in PSIM beside a HyperStudy study showing a 3D response surface plot
Figure 1View of project in PSIM (left) and HyperStudy (right).

As a reminder, this analysis is related to a buck converter with an input of 11.1 V and a desired output of 5.55 V. The resistance, capacitance, and inductance of the ideal passive elements are the parametric values that can be tuned, in addition to the duty cycle of the switching signal.

PSIM buck converter schematic with probes and a parameter file setting the inductance, capacitance, load and duty cycle
Figure 2Circuit schematic in PSIM to be optimized.

Optimization Setup

Before users can run this type of optimization in HyperStudy, a few basic steps are required to customize the analysis to your needs. Much of the information related to the model definition (the schematic, parameters, nominal values, etc.) is automatically populated if you create the Optimization using the setup outlined in the previous blog post as the starting definition.

HyperStudy Add dialog creating an Optimization approach that uses the existing study setup as its definition
Figure 3Creating an Optimization from our existing model in HyperStudy.

These values will automatically match the values provided in the initial setup, but they can be changed if desired. For this example, we will keep the nominal inputs, parameter bounds, and output responses (overshoot percentage and steady state voltage) as before.

HyperStudy input variable table with lower, nominal and upper bounds for C1, L1, R1 and the duty cycle V_D
Figure 4Parameter ranges in HyperStudy.

For our output responses, we can also now define goals and constraints. For this simple buck converter, we will provide a goal to minimize the overshoot percentage. We can also define multiple constraints to the steady state voltage value; we will use Steady State ≥ 5.5 V and Steady State ≤ 5.6 V to attempt to keep the output ripple within approximately ±1%.

HyperStudy output responses with goals: minimize overshoot and keep steady-state voltage between 5.5 and 5.6 V
Figure 5Defining optimization goals and constraints in HyperStudy.

Running the Optimization

Once the model has been defined, the parameter ranges are determined, and the goals have been established, you are almost ready to run your optimization. The final steps are to choose exactly how HyperStudy will perform the optimization. These options include localized and global gradient searches, single and multi-objective handling, and reliability analysis.

HyperStudy optimization method list with Global Response Search Method selected
Figure 6Optimization methods available in HyperStudy.

For this example, we will choose the Global Response Search method; we can also choose the number of iterations and failed evaluation handling. These give instructions on how many simulations to perform and how to handle results that do not align with the desired optimization boundaries, respectively.

HyperStudy Specifications step set to Global Response Search with 50 evaluations and failed runs ignored
Figure 7Choosing Optimization settings in HyperStudy.

HyperStudy also gives us the ability to define a number of simulations to run simultaneously, allowing us to get our results even more rapidly. Based on your hardware available, you can utilize parallel execution to evaluate multiple designs at the same time instead of waiting for each run to finish sequentially. Now we are ready to run the optimization!

HyperStudy Evaluate step with the Multi-Execution setting for parallel runs and the Run Tasks button highlighted
Figure 8Setting parallel tasks and launching optimization in HyperStudy.

Optimization Post-Processing

Now that the optimization is complete, HyperStudy gives us many options to analyze the results and extract valuable information. First, we can review a summary of the combinations of parameters that were searched and tested, in addition to the outputs of each simulation and how they performed against the constraints and goals. This table also includes the resulting optimal values based on the settings we have defined.

HyperStudy evaluation table with constraint-violating runs in red and the optimal run highlighted in green
Figure 9Summarized results in HyperStudy with optimal parameters highlighted in green.

We can also display a variety of results, such as iteration plots, scatter plots, and other tools to see how the optimization arrived at its results. These outputs also allow us to visualize which parameters had the most significant impact on various measurements or goals. For example, we can display a 3D plot showcasing the combination of effects from various inputs, and we can generate a trade-off plot, comparing the results of both output metrics.

HyperStudy 3D scatter plot of every evaluated design beside a trade-off plot of two output responses
Figure 103D scatter plot and output trade-off plot in HyperStudy.

We can also test the new optimized values back in the original PSIM software to see the full scope of the results.

PSIM output voltage plot: the optimized design (green) overshoots less and settles lower than the original (red)
Figure 11Optimized results (green) verified against non-optimal results (red) in PSIM.

This is just the beginning of what’s possible when you combine the circuit analysis capabilities of Simcenter PSIM with the design exploration and optimization strengths of Simcenter HyperStudy. Perhaps you would like to take this a step further on your end and add a closed loop controller, which can also be optimized in HyperStudy. Be sure to continue checking out these blogs for more information about this powerful cooperation between these tools, and remember to subscribe to our YouTube channel for even more content like this. If you have any specific questions about either of these tools, how they work together, or how other tools we offer may benefit you, please contact us directly!

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TrueInsight Engineering Team

Written by the application engineers who run TrueInsight demos, onboarding and support for Altair and Siemens users. Have a question about this workflow on your own model?

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