sogno-platform/dpsim
Jun 2026 – Jun 2026
1 Month active
Languages Used
C++CMakePython
Technical Skills
Build ConfigurationC++C++ developmentCMakeData validationElectrical Engineering
matthiasmees
PROFILE
Matthiasmees
Developed foundational EMT SSN components and usage notebooks for the sogno-platform/dpsim repository, focusing on scalable power systems simulation. Leveraging C++, Python, and CMake, the work introduced validated multi-terminal SSN implementations and enhanced the power-flow solver with a warm-start capability to improve performance and accuracy. The approach emphasized robust data validation and numerical analysis, including benchmarking against MNA components and case14 references. Code quality was strengthened by refining object-oriented structures and ensuring proper polymorphic behavior. Streamlining the build process reduced maintenance risk, while comprehensive Jupyter notebooks facilitated experimentation and reproducibility for advanced grid modeling and simulation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
5
Lines of code
6,083
Activity Months1
Your Network
72 peopleSame Organization
@rwth-aachen.de59
Work History
June 2026
7 Commits • 5 Features
Jun 1, 2026June 2026: Delivered foundational EMT SSN components and usage notebooks, established a validated multi-terminal SSN implementation, and enhanced the power-flow workflow with a warm-start capability. A focused build cleanup and code-quality improvements reduced maintenance risk and prepared the codebase for scalable grid modeling efforts. These changes accelerate experimentation, improve solution accuracy, and strengthen benchmarking against MNA components and case14 references.
7 Commits • 5 Features
Jun 1, 2026June 2026: Delivered foundational EMT SSN components and usage notebooks, established a validated multi-terminal SSN implementation, and enhanced the power-flow workflow with a warm-start capability. A focused build cleanup and code-quality improvements reduced maintenance risk and prepared the codebase for scalable grid modeling efforts. These changes accelerate experimentation, improve solution accuracy, and strengthen benchmarking against MNA components and case14 references.
June 2026
Activity
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Quality Metrics
Correctness88.6%
Maintainability85.8%
Architecture85.8%
Performance85.8%
AI Usage28.6%
Skills & Technologies
Programming Languages
C++CMakePython
Technical Skills
Build ConfigurationC++C++ developmentCMakeData validationElectrical EngineeringElectrical engineeringJupyter NotebooksNumerical analysisPython developmentPython programmingSimulation ModelingSimulation modelingalgorithm optimizationdata analysis
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