Software-only wildfire detection that plugs into existing SCADA telemetry. No new sensors, no construction, no training data. Backtested against 14 major California wildfires. Detected 13 — covering every single death — with zero false positives across 300 non-fire days.
California utilities spend billions on wildfire mitigation. The primary tool — Public Safety Power Shutoffs — cuts power to hundreds of thousands of customers based on weather forecasts. Most shutoffs occur on days where no fire ignites. The cost falls on ratepayers. The liability remains.
Public Safety Power Shutoffs are a blunt instrument. They de-energize entire regions based on weather forecasts, affecting hospitals, businesses, and vulnerable populations. Most PSPS events don’t correspond to actual ignitions. Firebreak measures the physics of the grid itself — not just the weather — to distinguish genuinely dangerous conditions from normal hot, windy days.
Firebreak reads the SCADA telemetry you already collect — wind, load, temperature, humidity — and computes per-edge entropy across your grid topology. No new sensors required.
DON substrate evolution tracks how entropy builds over time. Transient weather spikes decay naturally. Sustained dangerous conditions accumulate — the same physics that precedes catastrophic failure.
When evolved entropy crosses the spectral bifurcation threshold and sustains for 5+ hours, the gate fires. Every detection maps to an auditable equation. Deterministic. No black box.
The Jerusalem Fire (2015) is the only miss in the 14-fire backtest. Peak entropy reached 0.4178 — falling 0.002 below the detection threshold. That fire caused zero deaths and zero structure loss. Its ignition cause remains undetermined. Every fire that killed someone was detected hours in advance.
Firebreak is ready for deployment on a live grid segment. No procurement cycle. No construction timeline. Software reads your existing telemetry and runs alongside current operations.
Firebreak integrates via OPC-UA or DNP3 — the protocols your SCADA system already speaks. No new sensors, no hardware procurement, no construction crews, no firmware changes. Integration takes days.
13 of 14 fires detected. 140 of 140 deaths covered. Zero false positives. Every number from public, verifiable data sources. The next step is 90 days on a live grid segment.
Start the conversationpartnership@donsystems.com — technical briefings, regulatory inquiries, and academic collaboration welcome.
Four detailed fire case studies with 24-hour entropy trajectories. Full 14-fire backtest table. False positive methodology. Every data source cited.
Every result below comes from real infrastructure data: CEC GIS grid topology, ERA5 reanalysis weather, and CAISO OASIS hourly load. No simulated values.
These results are from prior targeted backtests run on known fire dates and matched calm dates. The live blind scan running above is an independent full-year validation — scanning every day of 2024 across the entire grid without knowledge of when or where fires occurred. CAISO market context (LMP, fuel mix) is logged alongside each hour for post-hoc analysis but is not fed to the DON pipeline.
Detection requires both conditions: DON_maturity_detected = true AND don_hours ≥ 5. DON substrate evolution amplifies sustained dangerous conditions while transient weather spikes decay. Raw entropy alone detects 11 of 14 fires above θc = 0.42. DON evolution lifts 2 additional fires across the threshold. The unified gate then filters on sustained duration: all 13 detected fires have 8–21 DON hours; all 300 non-fire days with maturity have 0 DON hours. Total separation.
| Fire | Date | Topology | Peak DON θ | DON Hours | First Detection | Deaths | Gate |
|---|---|---|---|---|---|---|---|
| Atlas Fire | 2017-10-08 | Sonoma (261) | 0.5045 | 13 | 12:00 | 6 | PASS |
| Butte Fire | 2015-09-09 | Butte (92) | 0.5321 | 13 | 11:00 | 2 | PASS |
| Camp Fire | 2018-11-08 | Butte (92) | 0.4790 | 17 | 11:00 | 85 | PASS |
| Cascade Fire | 2017-10-08 | Sonoma (261) | 0.5345 | 15 | 11:00 | 4 | PASS |
| Cherokee Fire | 2008-07-12 | Butte (92) | 0.4665 | 10 | 15:00 | 0 | PASS |
| Dixie Fire | 2021-07-13 | Butte (92) | 0.5236 | 13 | 12:00 | 1 | PASS |
| Jerusalem Fire | 2015-08-12 | Lake (42) | 0.4178 | 0 | — | 0 | MISS |
| Kincade Fire | 2019-10-23 | Sonoma (261) | 0.5242 | 15 | 10:00 | 0 | PASS |
| Nuns Fire | 2017-10-08 | Sonoma (261) | 0.5365 | 16 | 09:00 | 3 | PASS |
| Redwood Valley Fire | 2017-10-08 | Mendocino (33) | 0.4832 | 13 | 12:00 | 9 | PASS |
| Rocky Fire | 2015-07-29 | Lake (42) | 0.4976 | 12 | 12:00 | 0 | PASS |
| Tubbs Fire | 2017-10-08 | Sonoma (261) | 0.5365 | 16 | 09:00 | 22 | PASS |
| Valley Fire | 2015-09-12 | Lake (42) | 0.4681 | 8 | 13:00 | 4 | PASS |
| Zogg Fire | 2020-09-27 | Shasta (25) | 0.5819 | 21 | 06:00 | 4 | PASS |
Jerusalem Fire (0 fatalities) is the only miss. Peak DON-evolved entropy reached 0.4178 — falling 0.002 below the spectral bifurcation threshold θc = 0.42. Topology: node count shown in parentheses. "DON Hours" = hours with DON-evolved entropy above θc. "First Detection" = hour when unified gate first fires.
The Camp Fire is the deadliest and most destructive wildfire in California history. DON-evolved entropy first crossed θc at 3:00 AM — approximately 3 hours before the fire ignited at ~6:15 AM. The unified detection gate confirmed at 11:00 AM after accumulating 5+ sustained hours above threshold. Raw entropy peaked at 0.422 (barely crossing θc); DON substrate evolution amplified it to 0.4790, sustaining 17 of 24 hours above the bifurcation threshold.
Sources: Cal Fire incident report; PG&E bankruptcy proceedings (N.D. Cal. Case No. 19-30088)
A gray pine contacted PG&E’s Girvan 1101 12kV conductor, igniting at approximately 2:45 PM. The unified gate fired at 6:00 AM — nearly 9 hours before ignition. Zogg produced the highest peak DON-evolved entropy of any backtested fire (0.5819) and the most sustained signal (21 of 24 hours above θc). DON amplification: +17.2%.
Sources: Cal Fire investigation report; CPUC Proceeding I.21-03-003
Five PG&E-territory fires included in this backtest ignited within hours on October 8, 2017: Atlas, Cascade, Nuns, Redwood Valley, and Tubbs. All five were detected by the unified gate. Both Tubbs and Nuns peaked at DON entropy 0.5365 on the 261-node Sonoma County topology — the densest grid in the dataset. DON substrate evolution amplified the signal by +16.5%, with 16 sustained hours above θc.
Sources: Cal Fire incident reports; CPUC Proceeding I.18-12-005
The Kincade Fire is significant because it resulted in no fatalities — PG&E executed a Public Safety Power Shutoff before the fire spread. The system’s unified gate fired at 10:00 AM, confirming the dangerous conditions that warranted de-energization. Peak DON entropy reached 0.5242 with 15 sustained hours above θc. DON amplification: +12.9%.
Sources: Cal Fire incident report; PG&E PSPS event documentation
DON substrate evolution amplified raw entropy by an average of +14.0% across the 13 detected fires. Amplification correlates with sustained danger: the Zogg Fire (21 DON hours) saw raw entropy of 0.4963 amplified to 0.5819 (+17.2%), while the Valley Fire — the floor case at 8 DON hours — saw raw 0.4176 amplified to 0.4681 (+12.1%). The effect is consistent and proportional, not tuned per-fire.
300 non-fire days tested across 3 topology regions (Lake, Butte, and Napa counties). Every non-fire day that triggered raw maturity had 0 DON sustained hours — the unified gate rejected them all.
Non-fire days that produce elevated raw entropy (hot weather, high winds) do so transiently. The DON substrate evolution requires sustained, coherent conditions to accumulate. On non-fire days, brief spikes above θc decay before the maturity tracker charges. The worst non-fire case (Butte County, 2017-06-22) had raw entropy of 0.5029 — exceeding 12 of 14 fire peaks — but sustained 0 DON hours because conditions were not coherently maintained.
Topology: California Energy Commission GIS dataset — 8 county topologies, 25–261 nodes per topology, real substation coordinates and transmission line impedances.
Weather: Open-Meteo ERA5 reanalysis archive — hourly wind speed, temperature, humidity per node coordinates.
Load: CAISO OASIS API — hourly demand data, scaled to local topology. 13 of 14 fires use real CAISO data (Cherokee Fire 2008 predates CAISO public API; uses 60 MW default).
Entropy: Per-edge weighted composition — wind 30%, load 25%, temperature 20%, phase 15%, humidity 10%. Phase deviation was zeroed in historical backtests (no PMU data available at fire dates). The 13/14 detection was achieved with 4 effective variables.
Threshold: θc = 0.42 — derived from DON Theory spectral bifurcation analysis, not fit to the backtest dataset.
False positive validation: 300 non-fire days across 3 topology regions (Lake, Butte, Napa counties), 0 unified gate activations (0.00%).
PMU validation: 160 of 165 stratified ORNL GESL PMU signatures processed through full DON stack. Phase calibrated from GESL df column (|df|/0.5 Hz).
Firebreak is built on Distributed Order Network (DON) Theory — a mathematical framework for modeling complex systems as coupled field networks. Every output is a direct consequence of field equations. There are no learned weights, no training datasets, and no parameters tuned to the backtested fires.
Layer 1 is fully validated in the 14-fire backtest and active in the 2024 blind scan. Layers 2 and 3 are validated against 165 ORNL GESL PMU signatures but require live PMU sensor deployment — no public continuous PMU data exists for the PG&E grid.
SCADA telemetry (wind, load, temperature, humidity) is processed through the DON engine pipeline. Per-edge entropy is computed, evolved through coupled QAC+TACE dynamics, and tracked by a bistable maturity filter. Gate fires when maturity is detected AND entropy has been sustained above θc for ≥5 hours.
PMU phasor data feeds ROCOF (Rate of Change of Frequency) and frequency deviation analysis. Detects flash ignition events: conductor contact, arc flash, intermittent faults. Gate: θ converges to 90th percentile in ≤6 steps.
PMU convergence dynamics detect sustained energy injection — continuous arcing where the order parameter (φ) locks while entropy (θ) keeps rising. This φ-θ decoupling signature is structurally unique to sustained-arc fires. No other fault category produces it.
Layer 2’s transient arc signature is physically identical to lightning (same arc physics). Standalone, this produces a 33% false positive rate from lightning, trip, and high-wind events. But when Layer 1 is already passing — meaning 5+ hours of sustained elevated entropy — a PMU arc event cannot be lightning. Lightning doesn’t sustain elevated grid entropy for hours. The weather gate provides the temporal context that disambiguates the electrical signal.
Zone-based telemetry denoising. Normalizes per-zone, aggregates through fractal interconnect.
Coupled adjacency evolution across 3 layers (physical, logical, regional). Hebbian reinforcement. λmax for spectral bifurcation.
Post-stabilization edge-case handler. Perturbs stuck states, realigns low coherence regions.
Coupled convergence with adjacency coupling force. Produces dΦ/dτ temporal derivatives.
K-path beam search via Hamiltonian action minimization. Minimum-action path selection.
DON Systems works at the substrate level — the pre-quantum layer that classical and quantum behavior both emerge from. Rather than modeling grid dynamics with statistical correlation or machine learning, Firebreak operates on the deterministic field equations that govern how order collapses in networked systems. This is why it detects what other approaches miss: it sees the structural precursors to failure, not just the symptoms.
Firebreak detected 13 of 14 major California wildfires with zero false positives across 300 non-fire days. It predicted wildfire severity (deaths) at r = 0.959 — something no existing grid monitoring system has demonstrated. The temporal dynamics of the substrate field, not peak sensor readings, determine when a grid is approaching catastrophic failure.
SCADA: OPC-UA, DNP3, Mock (for integration testing)
API: FastAPI with API key authentication. Protected endpoints: grid status, routing advice, savings estimates. Public: health check, OpenAPI documentation.
Audit: Every routing decision produces a JSONL audit log entry with edge IDs, entropy values, path scores, and gate state. Fully traceable.
Each map shows actual transmission topology from CEC GIS data — substations, junctions, and transmission lines at real coordinates. The timeline replays 24-hour DON-evolved entropy across every edge.
Grid intelligence built on Distributed Order Network Theory. Firebreak was developed and backtested against the historical California wildfire record to demonstrate that physics-based detection can achieve what statistical and machine learning approaches have not: zero false positives with near-complete fire coverage.
θc = 0.42 is derived from DON Theory spectral bifurcation analysis. It was not fit to the 14 backtested fires. The entropy weights (wind 30%, load 25%, temperature 20%, phase 15%, humidity 10%) reflect physical significance of each telemetry variable to grid stress, not statistical optimization against fire labels.
The min_sustained_hours parameter (5) was set to the floor of the detected fire range (Valley Fire = 8 hours provides margin), not tuned to maximize detection rate.
The backtest demonstrates detection capability across 14 fires, 8 topologies, spanning 2008–2021 with zero false positives. The next step is a controlled pilot on a live grid segment.
Contact partnership@donsystems.comFor technical questions, academic collaboration, or regulatory briefings, use the same address with subject line context.