Helmholtz-AI-Energy/PowerDiT-Dataset

PowerDiT Dataset

This repository contains processed power-system graph data for machine learning experiments on power-flow- and optimal-power-flow-related tasks.
The data is generated with gridfm-datakit based on benchmark grid cases from PGLib, and then preprocessed into graph-structured samples. It is intended to be stored in the folders examples/data_processed/pf and examples/data_processed/opf.

Overview

The dataset consists of graph representations of electric power systems under a range of operating conditions and perturbations.

Each sample corresponds to a power-grid state derived from a benchmark network case, with:

• nodes representing buses
• edges representing transmission lines
• node features encoding electrical state, bus metadata, and generator properties.
• edge features encoding electrical line parameters.

The data is split into two different datasets:

• PF mode (Power Flow)
  The samples are merely physically (power-flow) consistent with non-cost-optimal generations.

• OPF mode (Optimal Power Flow)
  The samples are creating using an opf-solver such that generator setpoints are cost-optimal.

Available data

The processed dataset currently includes the following grid cases and sample counts:

grid	#buses	#gens	#lines	pf samples	opf samples
case3_lmbd	3	3	6	1,224,988	934,045
case5_pjm	5	5	12	1,287,400	1,214,688
case14_ieee	14	5	40	1,290,095	1,300,186
case24_ieee_rts	24	33	76	1,290,126	1,301,982
case30_ieee	30	6	82	1,300,680	1,296,767
case30_as	30	6	82	1,300,200	1,284,562
case39_epri	39	10	92	1,300,211	1,301,635
case57_ieee	57	7	160	1,301,222	1,273,026
case60_c	60	23	176	1,302,069	1,300,858
case73_ieee_rts	73	99	240	1,300,405	1,301,531
case89_pegase	89	12	420	654,591	1,483,225
case118_ieee	118	54	372	1,300,742	1,301,781
case162_ieee_dtc	162	12	568	1,046,318	181,038
case179_goc	179	29	526	1,293,617	739,821
case197_snem	197	35	572	1,301,323	1,391,101
case200_activ	200	49	490	1,301,480	1,320,560
case240_pserc	240	143	896	1,011,541	260,102
case300_ieee	300	69	822	1,067,772	625,028
case500_goc	500	224	1,466	1,018,707	543,710
case588_sdet	588	167	1,372	1,427,568	622,209
case793_goc	793	214	1,826	1,414,878	405,521

What the data consists of

At a high level, the dataset contains:

• benchmark power-grid topologies
• sampled and perturbed operating conditions
• simulated physical solutions
• graph-structured representations for ML

Node features

#	Feature	Name	PF-PQ	PF-PV	PF-REF	OPF-PQ	OPF-PV	OPF-REF	SE-PQ	SE-PV	SE-REF
0	P_d	Active load demand	I	I	I	I	I	I	M	M	M
1	Q_d	Reactive load demand	I	I	I	I	I	I	M	M	M
2	Q_g	Reactive generation	I	O	O	I	O	O	M	M	M
3	V_m	Voltage magnitude	O	I	O	O	O	O	M	M	M
4	V_a	Voltage angle	O	O	I	O	O	I	O	O	O
5	PQ flag	PQ bus indicator	I	I	I	I	I	I	I	I	I
6	PV flag	PV bus indicator	I	I	I	I	I	I	I	I	I
7	REF flag	Slack bus indicator	I	I	I	I	I	I	I	I	I
8	V_m^min	Min. voltage magnitude	--	--	--	I	I	I	I	I	I
9	V_m^max	Max. voltage magnitude	--	--	--	I	I	I	I	I	I
10	Q_g^min	Min. reactive generation	--	--	--	I	I	I	I	I	I
11	Q_g^max	Max. reactive generation	--	--	--	I	I	I	I	I	I
12	G_s	Shunt conductance	--	--	--	I	I	I	I	I	I
13	B_s	Shunt susceptance	--	--	--	I	I	I	I	I	I
14	V_n (kV)	Nominal voltage	--	--	--	I	I	I	I	I	I

Generator features

#	Feature	Name	PF-PQ/PV	PF-REF	OPF	State Est.
0	P_g	Active generation	I	O	O	M
1	P_g^min	Min. active generation	--	--	I	I
2	P_g^max	Max. active generation	--	--	I	I
3	c_0	Cost constant term	--	--	I	I
4	c_1	Cost linear coefficient	--	--	I	I
5	c_2	Cost quadratic coefficient	--	--	I	I

Edge features

#	Feature	Name	Power Flow	OPF	State Est.
0	P_e	Active power flow	O	O	M
1	Q_e	Reactive power flow	O	O	M
2	Y_ff,r	From-from admittance (real)	I	I	I
3	Y_ff,i	From-from admittance (imag.)	I	I	I
4	Y_ft,r	From-to admittance (real)	I	I	I
5	Y_ft,i	From-to admittance (imag.)	I	I	I
6	tap	Transformer tap ratio	I	I	I
7	theta_min	Min. angle difference	--	I	I
8	theta_max	Max. angle difference	--	I	I
9	rate_A	Thermal rating (MVA)	--	I	I

• I = input feature
• O = output / target feature
• M = measured feature
• -- = not used

Data source

The samples have been generated using Gridfm-datakit on the Future Technologies Partition on four intel sapphire rapids nodes. The underlying network cases (topologies and initial load and generation profiles) are based on PGLib benchmark systems, the perturbation framework is Gridfm-datakit. The preprocessing from Gridfm-graphkit has been adapted to allow for larger dataset sizes.

Perturbations applied during data generation

The data generation process in gridfm-datakit applies perturbations to create diverse operating conditions and topologies.

The perturbation types include:

1. Load perturbations

Load levels are randomly varied using a global scaling factor and local noise.

2. Topology and admittance perturbations

N-k perturbations are used to randomly cut off buses or edges. We used k=1. Further, the resistance and reactance parameters are randomly scaled during the perturbation process.

3. Generation perturbations

The generators are perturbed by changing their cost coefficients. In OPF-mode, the generator setpoints are recalculated with an opf-solver after topology and load perturbations have been applied. In PF-mode, an OPF-solver is only used BEFORE the perturbations.

Preprocessing

The raw power-system scenarios are preprocessed into a disk-backed heterogeneous graph format for efficient training and random access.

Raw inputs

The preprocessing pipeline starts from three parquet tables per dataset split:

• bus_data.parquet
• gen_data.parquet
• branch_data.parquet

Each row belongs to a specific scenario, and each scenario corresponds to one graph sample.

Scenario validation and filtering

Before graph construction, scenarios are validated for structural consistency.

In particular, scenarios are removed if their bus indices are invalid, i.e. if:

• bus IDs are duplicated, or
• bus IDs are not consecutive from 0 to N-1

This ensures that each scenario can be converted into a well-formed graph with a consistent node indexing scheme.

Feature extraction

The preprocessing selects fixed feature subsets for buses, generators, and branches.

Bus features

Bus node features include electrical state, bus type indicators, and operating constraints:

• Pd, Qd
• Qg, Vm, Va
• PQ, PV, REF flags
• min_vm_pu, max_vm_pu
• min_q_mvar, max_q_mvar
• GS, BS
• vn_kv

Generator features

Generator node features include operating quantities and cost parameters:

• p_mw
• min_p_mw, max_p_mw
• cp0_eur, cp1_eur_per_mw, cp2_eur_per_mw2
• in_service

Branch features

Branch edge features include power-flow quantities, admittance terms, and branch constraints.

For each physical branch, the preprocessing creates two directed edges:

• one forward edge (from_bus -> to_bus)
• one reverse edge (to_bus -> from_bus)

The forward and reverse edges receive direction-specific attributes, e.g.:

• forward: pf, qf, Yff_*, Yft_*
• reverse: pt, qt, Ytt_*, Ytf_*

Shared branch attributes include:

• tap
• ang_min, ang_max
• rate_a
• br_status

Bus-level aggregation of generator limits

Reactive power limits from generators are aggregated onto buses before graph construction.

For each (scenario, bus), the preprocessing sums:

• min_q_mvar
• max_q_mvar

across all generators connected to that bus, and merges these totals into the bus feature table.

This enriches bus nodes with bus-level reactive capability information.

Target construction

Task labels are derived from subsets of the selected features.

• Bus targets are taken from the leading subset of bus features
• Generator targets are taken from the leading subset of generator features
• Branch targets are stored separately as edge-level flow quantities

The exact interpretation of inputs and outputs depends on the downstream task (e.g. PF, OPF, state estimation, reconstruction).

Heterogeneous graph construction

Each scenario is converted into a HeteroData graph with:

• bus nodes
• generator nodes
• bus-to-bus edges for physical branches
• generator-to-bus edges
• bus-to-generator edges

This yields a heterogeneous graph representation that preserves both the electrical network structure and generator attachment structure.

Storage format

Instead of storing one file per scenario, the processed dataset is written into a small number of large NumPy memory-mapped arrays (memmap).

This avoids metadata overhead on large cluster file systems and enables efficient random access.

The processed directory contains arrays such as:

• bus_x.npy, bus_y.npy
• gen_x.npy, gen_y.npy
• edge_index.npy, edge_attr.npy, edge_y.npy
• gen_bus_ei.npy, bus_gen_ei.npy

along with an index.pt file containing per-scenario offsets.

Using these offsets, a single scenario can be loaded without materializing the full dataset in memory.

Runtime loading and normalization

When a sample is accessed:

1. the corresponding slices are read from the memmap arrays,
2. a PyTorch Geometric HeteroData object is reconstructed,
3. edge indices are converted into PyG format,
4. feature normalization is applied at access time.

Normalization is therefore not baked into the stored arrays, but applied dynamically during dataset loading.