State Estimation and Measurements Reference

Measurement

Generic state-estimation measurement model.

Fields:

• typ: Measurement type.
• value: Measured value (Vm in p.u., powers in MW/MVar).
• sigma: Standard deviation in measurement units.
• weight: Weight used in WLS (1/sigma^2).
• active: If false, measurement is ignored by estimator.
• busIdx: Bus index for bus measurements.
• branchIdx: Branch index for branch flow measurements.
• direction: Branch direction :from or :to, otherwise :none.
• id: Optional measurement identifier.

@enum MeasurementType

Supported measurement types for the first WLS state-estimation implementation.

addMeasurement!(measurements; typ, value, sigma, active=true, busIdx=nothing, branchIdx=nothing, direction=:none, id="")

Append a state-estimation measurement to measurements and return it.

addPflowMeasurement!(measurements; net, value, sigma, direction=:from, branchNr=nothing, fromBus=nothing, toBus=nothing, active=true, id="")

Append an active-power flow measurement identified by branchNr or a unique fromBus/toBus branch pair.

addPinjMeasurement!(measurements; net, busName, value, sigma, active=true, id="")

Append an active-power injection measurement identified by busName.

addQflowMeasurement!(measurements; net, value, sigma, direction=:from, branchNr=nothing, fromBus=nothing, toBus=nothing, active=true, id="")

Append a reactive-power flow measurement identified by branchNr or a unique fromBus/toBus branch pair.

addQinjMeasurement!(measurements; net, busName, value, sigma, active=true, id="")

Append a reactive-power injection measurement identified by busName.

addVmMeasurement!(measurements; net, busName, value, sigma, active=true, id="")

Append a bus voltage-magnitude measurement identified by busName.

addZeroInjectionMeasurements!(measurements; net, sigma=1e-6, busNames=nothing, busIdxs=nothing, active=true, idPrefix="ZI") -> Vector{Measurement}

Append active- and reactive-power zero-injection pseudo-measurements for the selected buses and return the newly added measurements.

Selection rules:

• If busIdxs is provided, those indices are used.
• Else if busNames is provided, names are resolved to indices.
• Else passive buses are detected automatically via findPassiveBuses(net).

These pseudo-measurements are the current way to encode equality constraints P_inj = 0 and Q_inj = 0 in the WLS estimator.

findPassiveBuses(net; atol=1e-9, includeSlack=false) -> Vector{Int}

Return bus indices that have no generation, no load, and no shunt contribution within the given tolerance atol.

This is useful for state-estimation workflows where passive / transit buses are often modeled through zero-injection pseudo-measurements.

generateMeasurementsFromPF(net; kwargs...) -> Vector{Measurement}

Generate synthetic measurements from the current solved network state.

Keyword options:

• includeVm, includePinj, includeQinj, includePflow, includeQflow
• noise: add Gaussian noise if true
• stddev: dictionary from MeasurementType => sigma
• rng: random number generator

measurementStdDevs(; vm=0.005, pinj=1.0, qinj=1.0, pflow=1.0, qflow=1.0)

Create default standard-deviation map for synthetic measurement generation.

SEResult

Result container for the first classical WLS state-estimation run.

evaluate_global_observability(net, measurements; kwargs...) -> NamedTuple

Evaluate global observability on active measurements using the finite-difference measurement Jacobian.

Includes global redundancy metrics

• redundancy = r = m - n
• redundancy_ratio = ρ = m / n
• dof = ν = m - n

Quality classes:

• :good: observable and no critical single measurement
• :critical: observable, but at least one single critical measurement (or ν <= 0)
• :not_observable: not observable

evaluate_local_observability(net, measurements, stateCols; kwargs...) -> NamedTuple

Evaluate local observability on selected Jacobian columns (stateCols).

Procedure:

1. Build global Jacobian H from currently active measurements.
2. Keep only rows that have at least one nonzero entry in the selected columns. These rows correspond to measurements that are locally sensitive to the requested states.
3. Evaluate observability/redundancy on the reduced matrix Hlocal.

Returned NamedTuple extends global metrics with:

• rows: selected row indices (within global active-Jacobian row numbering)
• stateCols: copied input state-column selection.

Interpretation:

• :good means local states are observable with positive redundancy and no single critical measurement.
• :critical means still observable but vulnerable to a single outage (or ν <= 0).
• :not_observable means local states cannot be uniquely reconstructed.

evaluate_local_observability_matrix(H, stateCols; tol=nothing) -> NamedTuple

Evaluate local observability on a matrix H restricted to selected stateCols.

evaluate_observability_matrix(H; tol=nothing) -> NamedTuple

Evaluate global observability and single-row criticality directly on a matrix H (without building a network model).

numeric_rank(A; tol=nothing) -> Int

Compute numerical matrix rank using singular values.

numerical_observable(H; tol=nothing) -> Bool

Numerical observability test on a Jacobian-like matrix H. Returns true when rank(H) == n (full column rank).

numerical_row_redundant(H, i; tol=nothing) -> Bool

Check if row i remains numerically redundant in H.

print_se_diagnostics(io, diag; topN=10)

Pretty-print diagnostics from validate_measurements or runse_diagnostics including:

• explanation of global_consistency
• tabular measurement ranking
• BAD/OK marker per measurement
• optional rerun comparison if present

runse!(net, measurements; kwargs...) -> SEResult

Run a first classical nonlinear weighted least-squares state estimator.

State representation:

• bus voltage angles for all non-slack buses (radians)
• bus voltage magnitudes for all buses (p.u.)

runse_diagnostics(net, measurements; deactivate_and_rerun=false, kwargs...) -> NamedTuple

Extended diagnostics workflow around validate_measurements with optional deactivate-and-rerun logic for the currently largest suspicious measurement.

structural_observable(H) -> Bool

Structural observability test on a Jacobian-like matrix H. Returns true when the maximum bipartite matching size equals the number of state columns n.

structural_row_redundant(H, i) -> Bool

Check if row i remains structurally redundant in H.

summarize_se_diagnostics(diag) -> NamedTuple

Create a compact interpretation summary for a diagnostics object returned by validate_measurements or runse_diagnostics.

global_consistency is interpreted as:

• true: SE converged and objective is inside χ²-like 3σ plausibility band
• false: either non-convergence or implausibly large objective

validate_measurements(net, measurements; kwargs...) -> NamedTuple

Run state-estimation diagnostics on currently active measurements and return a machine-readable report with:

• global bad-data consistency check (global_consistency)
• χ²-like objective plausibility summary
• largest-normalized-residual ranking
• suspicious measurement list (threshold-based)