Task — fault_location_global

benchmark version 1.1.0 · held_out results v1.0.0 · splits v1.0/held_out, v1.1/multi_grid
updated 2026-09-29 13:18 UTC · git 7243f310ddbc

tier 3 · fault_location · global view · regression · all cells measured

What this task is. Estimate how far along the line the fault sits, as a continuous distance regression. Crews are dispatched by this number; errors are reported as MAE in the grid's own distance units, lower is better, with the median absolute error as a robustness companion. Global view: every cubicle in the grid at once (width differs per grid), the wide-area upper bound on observability.

How it's scored

Headline metric mae (↓ lower is better); full metric set: mae · rmse · r2 · median_abs_error. Metrics are a frozen contract (src/evemtbench/evaluation/metrics.py); label derivation: src/evemtbench/tasks/labels.

Example window

example waveform window

event flt_1phg_shc · grid double_line (adapt_grid/test split) · sample 23, window #529, cubicle 3 (max-RMS) · 50 ms / 480 samples @ 9.6 kHz · top: 3-phase current, bottom: 3-phase voltage · figure provenance in assets/manifest.json

Results (held_out)

fault_location_global — every grid × baseline; click headers to sort
taskgridbaselineheadlinetestbenchmarkparamsfit/seedtrace
fault_location_globalcigre_mvmajoritymae ↓23.915 ± 0.000 (n=5)30.351 ± 0.000 (n=5)—0strace: output · eval-code · log · train-code · config · W&B
fault_location_globalcigre_mvrandom_forestmae ↓24.131 ± 0.047 (n=5)30.481 ± 0.083 (n=5)—12.6mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalcigre_mvmlpmae ↓22.196 ± 0.031 (n=5)18.868 ± 0.943 (n=5)42.9M9.6mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalcigre_mvgrumae ↓22.648 ± 0.381 (n=5)20.388 ± 2.245 (n=5)117k3.0mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalcigre_mvcnnmae ↓22.345 ± 0.302 (n=5)19.521 ± 0.995 (n=5)124k3.4mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalcigre_mvresnetmae ↓22.857 ± 0.448 (n=5)21.112 ± 2.293 (n=5)602k3.0mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_linemajoritymae ↓24.416 ± 0.000 (n=5)31.094 ± 0.000 (n=5)—0strace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_linerandom_forestmae ↓19.776 ± 0.039 (n=5)17.824 ± 0.092 (n=5)—4.8mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_linemlpmae ↓16.338 ± 0.125 (n=5)6.296 ± 0.269 (n=5)12.0M3.0mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_linegrumae ↓15.869 ± 0.359 (n=5)5.102 ± 0.715 (n=5)68k1.4mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_linecnnmae ↓17.771 ± 0.093 (n=5)10.106 ± 0.327 (n=5)67k1.7mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaldouble_lineresnetmae ↓17.445 ± 0.256 (n=5)8.267 ± 1.416 (n=5)530k3.8mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvmajoritymae ↓24.378 ± 0.000 (n=5)31.168 ± 0.000 (n=5)—0strace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvrandom_forestmae ↓21.830 ± 0.072 (n=5)23.947 ± 0.088 (n=5)—12.6mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvmlpmae ↓16.994 ± 0.043 (n=5)6.538 ± 0.115 (n=5)26.7M6.5mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvgrumae ↓17.359 ± 0.181 (n=5)7.804 ± 0.638 (n=5)92k2.3mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvcnnmae ↓18.590 ± 0.215 (n=5)11.585 ± 0.441 (n=5)94k3.2mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globaltestgrid_110kvresnetmae ↓18.794 ± 0.250 (n=5)12.354 ± 0.641 (n=5)564k2.6mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39majoritymae ↓25.224 ± 0.000 (n=5)31.129 ± 0.000 (n=5)—0strace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39random_forestmae ↓24.659 ± 0.057 (n=5)29.816 ± 0.039 (n=5)—1.3htrace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39mlpmae ↓20.697 ± 0.115 (n=5)20.389 ± 0.724 (n=5)103.4M33.6mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39grumae ↓23.203 ± 0.385 (n=5)24.926 ± 1.094 (n=5)211k9.4mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39cnnmae ↓21.447 ± 0.374 (n=5)27.133 ± 4.908 (n=5)234k12.9mtrace: output · eval-code · log · train-code · config · W&B
fault_location_globalieee39resnetmae ↓21.816 ± 0.698 (n=5)21.762 ± 3.878 (n=5)744k9.1mtrace: output · eval-code · log · train-code · config · W&B

Metric definitions

headline & reported metrics for this view — the metric set is a frozen contract; each links to the exact function that computes it

metricdefinitionentry
maemean absolute error, in the grid's own distance unitsevaluate()
rmseroot mean squared errorevaluate()
r2coefficient of determination 1 − SS_res/SS_totevaluate()
median_abs_errormedian of absolute errorsevaluate()

Reproduce one cell

PYTHONPATH=src python -m evemtbench.baselines.runner --task fault_location_global --protocol held_out \
    --grid <grid> --baseline <baseline> --seeds 0 1 2 3 4