Microgrids · Distribution systems
Physics-informed GNN for microgrid N-1 security prediction
This course builds power-flow foundations for physics-informed graph neural networks and microgrid N-1 security prediction. Start with Balanced Power Flow, explore the four-wire Unbalanced Power Flow lesson, then build and solve real pandapower networks in your browser.
Balanced Power Flow
Interactive lesson · Microgrids / Distribution systems
Explore balanced three-phase AC power flow through background, equation derivations, a worked feeder example, live parameter controls, and editable Python code.
Unbalanced Power Flow
Interactive lesson · Microgrids / Distribution systems
Derive a three-phase, four-wire feeder model and explore unequal loads, single-phase PV, neutral displacement, sequence components, and editable Python.
PandaPower-based Implementation
Interactive lesson · Real pandapower / Python
Build balanced and three-phase networks, change parameters, run editable Python, inspect result tables, and screen line outages. Download a self-contained Notebook for further experiments.