TopoStateGrid 1.1.2

TopoStateGrid is a physically informed graph construction method that converts power-grid topology, component attributes, and operating-state variables into machine-learning-ready graph datasets.

TopoStateGrid

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TopoStateGrid is a physically informed graph construction method that converts power-grid topology, component attributes, and operating-state variables into machine-learning-ready graph datasets.

The Python import name is:

import topostategrid

TopoStateGrid focuses on reusable graph dataset construction for power-system machine learning. It keeps the workflow small: load power-grid data, build graph samples, save them, split them, normalize features, and optionally render graph sequences for inspection.

What It Builds

Each graph sample represents:

G_t = (V, E, X_t, A_t, y_t)

where:

• V are bus nodes.
• E are physical line and transformer connections.
• X_t contains node features for a scenario or timestamp.
• A_t contains edge features for a scenario or timestamp.
• y_t is an optional label.

The output is a homogeneous bus-branch PyTorch Geometric Data object:

data.x
data.edge_index
data.edge_attr
data.y
data.network_id
data.sample_id
data.timestamp
data.scenario_id
data.contingency_id
data.node_feature_names
data.edge_feature_names
data.metadata

Edges are stored bidirectionally so message passing can use both branch directions. Source-specific metadata is stored as a JSON string, not a Python dictionary, so graphs from different input sources can be batched together with PyG DataLoader.

Installation

From PyPI:

python -m pip install TopoStateGrid

Optional extras:

python -m pip install "TopoStateGrid[pandapower]"
python -m pip install "TopoStateGrid[visual]"
python -m pip install "TopoStateGrid[test]"

From a local checkout:

git clone https://github.com/MingLeiZhou/TopoStateGrid.git
cd TopoStateGrid
python -m pip install -e ".[test,pandapower,visual]"

Supported Inputs

TopoStateGrid v1.1 supports:

Source	Status	Notes
OPFData JSON	Supported	Uses extracted JSON samples such as grid.nodes.bus and solved state fields when available.
MATPOWER / PGLib .m	Supported	Parses mpc.bus and mpc.branch; static files have zero flow fields unless solved state data are supplied elsewhere.
pandapower net	Supported, optional	Requires TopoStateGrid[pandapower]; supports buses, lines, and transformers.
pandas DataFrame tables	Supported	Useful for custom bus/branch/state tables.
CSV tables	Supported	Thin wrapper around DataFrame support.

The MATPOWER parser accepts comma-delimited or whitespace-delimited rows, semicolons, % comments, scientific notation, multi-line matrices, and explicit empty matrices such as mpc.branch = [ ];.

Feature Schema

Node feature order:

bus_status, bus_type, pd, qd, vm, va, vmax, vmin, normalized_demand

Edge feature order:

component_type, r, x, b_from, b_to, rate_a, pf, qf, pt, qt, loading_ratio, outage_flag

Missing optional numeric values are filled with zero after validation and NaN-safe conversion.

For pandapower line data, rate_a uses max_i_ka as an approximate rating proxy when no direct MVA rating is available.

Quick Start

Build a graph from pandas tables:

import pandas as pd
from topostategrid import build_graph_from_tables

bus_df = pd.DataFrame({
    "bus_id": [1, 2, 3],
    "bus_type": [3, 1, 1],
    "pd": [0.0, 1.5, 0.8],
    "qd": [0.0, 0.4, 0.2],
})

branch_df = pd.DataFrame({
    "from_bus": [1, 2],
    "to_bus": [2, 3],
    "r": [0.01, 0.02],
    "x": [0.05, 0.06],
})

data = build_graph_from_tables(
    bus_df,
    branch_df,
    network_id="toy_3bus",
    sample_id="sample_0",
)

print(data.x.shape)
print(data.edge_index.shape)
print(data.edge_attr.shape)

Build from CSV:

from topostategrid import build_graph_from_csv_tables

data = build_graph_from_csv_tables(
    "bus.csv",
    "branch.csv",
    network_id="toy_3bus",
)

Build from pandapower:

import pandapower as pp
from topostategrid import build_graph_from_pandapower

net = pp.create_empty_network()
b1 = pp.create_bus(net, vn_kv=110)
b2 = pp.create_bus(net, vn_kv=110)
b3 = pp.create_bus(net, vn_kv=110)

pp.create_ext_grid(net, b1)
pp.create_load(net, b2, p_mw=10.0, q_mvar=3.0)
pp.create_line_from_parameters(net, b1, b2, 1.0, 0.1, 0.2, 0.0, 0.4)
pp.create_line_from_parameters(net, b2, b3, 1.0, 0.1, 0.2, 0.0, 0.4)
pp.runpp(net)

data = build_graph_from_pandapower(net, network_id="pandapower_3bus")

Batch graphs from different sources:

from torch_geometric.loader import DataLoader

loader = DataLoader([graph_a, graph_b, graph_c], batch_size=3)
batch = next(iter(loader))

Operating-State Graphs

TopoStateGrid separates static topology from operating state:

• Static topology: buses, lines, transformers, branch parameters.
• Operating state: demand, voltage, angle, branch flow, loading ratio.

For one network, edge_index can remain fixed while data.x and data.edge_attr change across scenarios or timestamps. This supports later supervised prediction, self-supervised pretraining, temporal forecasting, and cross-topology evaluation.

Temporal Windows

Create windows from ordered graph samples:

from topostategrid import make_temporal_windows

windows = make_temporal_windows(
    graphs,
    input_window=6,
    forecast_horizon=1,
    target="y",
)

If all timestamps are valid and comparable, temporal utilities sort by timestamp. Otherwise they preserve input order.

Labels

TopoStateGrid can attach temporary stress proxy labels:

risk_score = max_line_loading_ratio
y_cls = 1 if max_line_loading_ratio > 1.0 else 0
y_reg = risk_score

These labels are simple helper labels for graph-construction experiments. They do not overwrite existing data.y, data.y_cls, data.y_reg, or data.risk_score unless overwrite=True is passed.

Splits And Normalization

Implemented split strategies:

• Random split
• Time-based split
• Leave-One-Network-Out split

LONO supports cross-topology evaluation, for example training on case14, case30, and case57, then testing on case118.

FeatureNormalizer fits node and edge statistics only on the training split and then transforms train/validation/test graphs with the same statistics to avoid data leakage.

Visualization

TopoStateGrid includes optional GIF/MP4 rendering and interactive HTML output for inspecting constructed graph sequences:

from topostategrid import render_graph_html, render_graph_sequence

render_graph_sequence(
    graphs,
    "outputs/topostategrid_sequence.gif",
    node_value="vm",
    edge_value="loading_ratio",
)

render_graph_html(
    graphs,
    "outputs/topostategrid_interactive.html",
    node_value="vm",
    edge_value="loading_ratio",
)

The HTML viewer supports zoom, pan, frame playback, a time slider, and hover tooltips for node and line feature values. The renderer visualizes existing graph samples. It does not simulate grid dynamics.

Demo preview:

Interactive demo file:

• docs/demo/topostategrid_case300_interactive.html

For the interactive view, download or clone the repository and open the HTML file in a browser. GitHub may display the HTML source instead of running it.

Large pandapower example:

python examples/08_render_large_pandapower_gif.py
python examples/09_render_interactive_html.py

This script uses pandapower case300, converts it to a 300-node TopoStateGrid graph sequence, and renders a 20-second GIF.

Example Scripts

Script	Purpose
examples/01_build_single_graph.py	Build one local OPFData graph and save it.
examples/02_build_multiple_state_graphs.py	Build multiple OPFData scenario graphs.
examples/03_create_temporal_windows.py	Create temporal graph windows.
examples/04_create_splits.py	Create random, time-based, and LONO splits.
examples/05_build_from_tables.py	Build a graph from in-memory pandas tables.
examples/06_build_from_pandapower.py	Build a graph from a small pandapower network.
examples/07_render_graph_animation.py	Render a small graph-state sequence to GIF.
examples/08_render_large_pandapower_gif.py	Render a 20-second GIF from pandapower case300.
examples/09_render_interactive_html.py	Render an interactive HTML viewer with node and line state tooltips.

The example scripts write generated artifacts to outputs/, which is intentionally ignored by git.

Testing

Run the standard test suite:

python -m unittest discover -s tests -q

Or with pytest:

pytest -q

On systems where the default matplotlib cache directory is not writable, set a writable cache directory:

MPLCONFIGDIR=/private/tmp/topostategrid-mpl pytest -q

pandapower may warn that numba is not installed. That warning only affects pandapower runtime speed.

Output Files

Common generated files:

outputs/
├── graphs.pt
├── metadata.csv
├── graphs_multi.pt
├── metadata_multi.csv
├── split_random.json
├── split_time.json
├── split_lono.json
├── temporal_windows.pt
├── graphs_tables.pt
├── graphs_pandapower.pt
├── topostategrid_sequence.gif
├── topostategrid_case300_20s.gif
├── topostategrid_interactive.html
├── topostategrid_case300_interactive.html
└── README_generated.md

Committed demo files are stored separately:

docs/demo/
├── topostategrid_case300_20s.gif
└── topostategrid_case300_interactive.html

Use topostategrid.export.load_graphs to load .pt graph files because it handles recent PyTorch weights_only defaults.

Current Scope

• Homogeneous bus-branch graph only.
• No .mat support.
• No heterogeneous component graph.
• pandapower line rating mapping may be approximate when only max_i_ka is available.
• MP4 rendering requires ffmpeg; GIF rendering uses matplotlib, networkx, and Pillow. HTML rendering is a standalone file and uses browser SVG/JavaScript.