SMIB with governor droop (4D)

ADVANTAGES1 · dim 4

SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →

4D Single-Machine Infinite-Bus with two-lag governor droop dynamics (reduced TGOV1). State: [delta, omega, P_m, P_valve]. Parameters: H=3.0, D=2.0, R=0.05, Tg=0.2s, Tt=0.5s.

Power & energy systems

Problem definition

Kundur (1994) Power System Stability and Control, Ch. 11-12; IEEE TGOV1; MathWorks SMIB example; Chandra Prasad & Nair (2016)

Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.

Show canonical RHS excerpt
def _smib_governor_rhs(t: float, y: np.ndarray) -> np.ndarray:
    """SMIB with governor droop dynamics.

    Pe = (E'q * V_inf / X'd) * sin(delta).  Reduced from IEEE TGOV1:
    two first-order lags (governor servo + turbine) with proportional droop.
    Simplification documented per board review 2026-04-15.
    """
    delta, omega, P_m, P_valve = y

    P_e = (_GOV_Eq * _GOV_Vinf / _GOV_Xdp) * np.sin(delta)

    ddelta = _GOV_ws * (omega - 1.0)
    domega = (1.0 / (2.0 * _GOV_H)) * (P_m - P_e - _GOV_D * (omega - 1.0))
    dP_m = (1.0 / _GOV_Tt) * (P_valve - P_m)
    dP_valve = (1.0 / _GOV_Tg) * (
        _GOV_Pref - (1.0 / _GOV_R) * (omega - 1.0) - P_valve
    )
    return np.array([ddelta, domega, dP_m, dP_valve])
Parameters
  • _GOV_D = 2
  • _GOV_Eq = 1.1
  • _GOV_H = 3
  • _GOV_Pref = 0.8
  • _GOV_R = 0.05
  • _GOV_Tg = 0.2
  • _GOV_Tt = 0.5
  • _GOV_Vinf = 1
  • _GOV_Xdp = 0.3
  • _GOV_ws = 376.991118431
Initial condition
y(0) = [0.219951014692, 1, 0.8, 0.8]
Horizon
t ∈ [0, 30]

Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.

Fingerprint

Spread: low

Default noise: medium

Recommendation snapshot

Clean best: SciPy BDF

Noisy best: SolvSRK

Coverage

14 solver arms · clean + 5 noise levels

Ranked on survival, precision, and speed

Versions & freeze

Methodology →
Freeze
2026-08-13
libsolvsrk
2.3.0
SciPy
1.14
SUNDIALS
CVODE (bundled backend)

20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: SMIB with governor droop (4D) (smib-with-governor-droop-4d)

Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.

Self-reported by Resonix Labs · not independently verified

Results matrix

Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.

Objective

Best overall trade-off of survival, precision, and speed.

Noise level

#SolverSurvivalSCDnfevWallScore
1SciPy BDFSciPy
100%
16.6862 ms1.000
2SciPy RadauSciPy
100%
16.655<1 ms1.000
3CVODE BDFexternal
100%
16.6104 ms1.000
4CVODE Adamsexternal
100%
16.6104 ms1.000
5FBDFexternal
100%
16.6113.45 s1.000
6SolvSRK
100%
16.63612 ms1.000
7TRBDF2external
100%
16.3213.72 s1.000
8Vern9external
100%
16.12743.13 s1.000
9SciPy LSODASciPy
100%
10.35<1 ms0.864
10Vern7external
100%
9.28422.76 s0.838
11SciPy DOP853SciPy
100%
8.29024 ms0.814
12SciPy RK23SciPy
100%
7.66083 ms0.800
13Tsit5external
100%
6.71,776661 ms0.779
14SciPy RK45SciPy
100%
6.01,1845 ms0.763

At Clean, best balanced arm is SciPy BDF · SolvSRK survival 100%, SCD 16.6.

Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →

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Cite this page

Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.

@misc{resonix_evidence_smib_with_governor_droop_4d_2026,
  title        = {Resonix Evidence Portal: SMIB with governor droop (4D)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/smib-with-governor-droop-4d}},
  note         = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}

Related

TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels

Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology

Self-reported by Resonix Labs · not independently verified · Verification status