fdia-graph¶

Stealthy FDIA localization datasets for power grids, PyTorch-ready in one line. Eight IEEE systems (14 / 30 / 57 / 89 / 118 / 145 / 200 / 300 buses), 72,000 records each.

import fdia_graph as fg

ds = fg.load("ieee118", split="train")     # auto-downloads + caches
loader = ds.loader(batch_size=64)
for batch in loader:
    batch["node_x"], batch["edge_x"], batch["edge_index"], batch["y"], batch["family"]

New here? docs/ROADMAP.md — the SDK/engine split + how the files connect · docs/reference/DATA_DICTIONARY.md — what every array means · docs/reference/CONCEPTS_TO_CODE.md — paper equations → functions · docs/reference/EXAMPLES.md — runnable baselines, streams, stats · docs/guides/state_estimation.md — beating WLS.

Install¶

pip install fdia-graph              # loader
pip install "fdia-graph[torch]"     # + PyTorch DataLoader
pip install "fdia-graph[pyg]"       # + torch_geometric
pip install "fdia-graph[generate]"  # + pandapower, to generate custom data

Data is pinned per SDK version and cached in ~/.cache/fdia_graph. Pin a version with fg.load(..., release="v0.7.2"); pip install --upgrade fdia-graph moves it forward.

Load¶

fg.load("ieee300", split="train")                              # 60/20/20 chronological split
fg.load("ieee118", split="test", families=["Aq","At","Al"])    # family subset
fg.load("ieee118", units="pu")                                 # per-unit + radians (default is physical)

Whole split at once: ds.to_numpy() / .to_torch() / .to_pandas(). Custom data: fg.generate(system, name, per_family=..., attack_intensity=..., ...) then fg.load(name). Continuous timeline for LSTM/TGN: fg.load_stream(system). Both in docs/reference/EXAMPLES.md.

State estimation¶

from fdia_graph.se import WLS, SubspacePrior   # pip install "fdia-graph[se]"

test = fg.load("ieee118", split="test")
est = SubspacePrior(rank_frac=0.5, reweight="huber", c=2.5).fit(fg.load("ieee118", split="train"))
xhat = est.estimate(test)          # [n, 2N-1] = [theta rad (non-slack) | V pu (all buses)]
print(est.score(test))             # per-family angle/voltage MAE vs the clean truth

WLS, AdaptiveWeighting, ResidualRemoval and SubspacePrior share one chord-Newton iteration, Jacobian and starting point and differ only in state space and weights — the audited protocol of the companion estimation paper, verified equivalent to its solver per record.

Data¶

Each record is a sparse measurement graph with N buses (nodes) and E branches (edges). Read a shape as "values per item": [N,4] = 4 numbers per bus, [E,8] = an 8-dim vector per branch, [2,E] = 2 rows × E branches. Full reference in docs/reference/DATA_DICTIONARY.md.

node_x [N,4] = [ |V|, P_inj, Q_inj, theta ]      bus meters      node_m [N,4] = mask
edge_x [E,2] = [ P_from, Q_from ]                branch flow     edge_m [E,2] = mask
edge_index [2,E] = [ from_bus; to_bus ]          connectivity
edge_attr  [E,8] = [ r,x,b,g,gs,bs,tap,shift ]   static line physics  (ds.edge_attr)
y [N] = attacked (1) / clean (0)                 localization target
swing [N,2], temporal_delta [N,2]                temporal features
clean [N,4] = [ |V|, P_inj, Q_inj, theta ]       noiseless truth, all buses (SE target, v0.7.2+)
edge_clean [E,2] = [ P_from, Q_from ]            noiseless true flows (unmetered branches zeroed)

Attacks¶

Three stealthy families that evade classical bad-data detection, plus three detectable ones as a contrast set. Every per-bus change stays in a plausibility band (≈2% noise floor to 20% cap); meter error follows an accuracy-class model.

family	attack	classical BDD
`Aq`	stealthy load rescale + AC re-solve	evades
`At`	slow temporal load ramp	evades
`Al`	targeted load redistribution (hides overloads)	evades
`Ad` / `As` / `Ar`	meter corruption / scaling / replay	caught

Report per-family node-F1 with the false-alarm rate, not accuracy (clean buses dominate). A lightweight per-bus MLP reaches ~0.92 localization macro-F1; see docs/reference/EXAMPLES.md.

Citation¶

Cite the attack- and measurement-model sources:

Yuan, Li & Ren, Modeling load redistribution attacks in power systems, IEEE T-SG 2(2), 2011. (LRA)
Haghshenas, Hasnat & Naeini, A Temporal GNN for Cyber Attack Detection and Localization in Smart Grids, IEEE ISGT 2023. (ramp)
Zaman & Lin, PING: Physics-Informed GNNs to Generalize FDIA Localization, NAPS 2025. (measurement model)
Asprou, Kyriakides & Albu, Variable Weights in a WLS State Estimator, IEEE T-IM 63, 2014. (meter noise)
Boyaci et al., Joint Detection and Localization of Stealth FDIA, IEEE T-SG, 2022. (protocol)

License¶

Data under CC BY 4.0, code under MIT (see LICENSE). Synthetic, from public IEEE cases — not for operational decisions.