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GP-EE-135KKNPElectricalReady

Distribution state estimation under sparse metering

An electrical engineering study of voltage-state estimation, bad-data detection, and uncertainty when distribution-feeder measurements are sparse or noisy.

Distribution state estimation under sparse metering project visual
GP-EE-135KKNP · Electrical
  • Python
  • pandapower
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Distribution feeders often have only a small number of direct voltage measurements, while many bus injections are represented by uncertain pseudo measurements.

The engineering problem is to estimate the complete feeder voltage state, measure the effect of meter density, identify corrupted observations, and report uncertainty without claiming field validation.

Project objectives

  • Build four repeatable operating cases on the IEEE 33-bus radial feeder.
  • Generate sparse voltage measurements and uncertain active-power and reactive-power pseudo measurements.
  • Compare weighted least squares with Huber robust weighted estimation.
  • Measure clean accuracy, corrupted-meter resilience, residual detection, interval width, and coverage.
  • Explain how meter density and feeder condition affect the evidence and its limitations.

Project structure

Project components

01

Feeder model

Builds the benchmark feeder, applies each operating scenario, solves AC load flow, and records voltage truth.

02

Sensitivity model

Perturbs active and reactive injections to construct a transparent local voltage-sensitivity matrix.

03

Measurement simulator

Creates seeded pseudo measurements, sparse voltage meters, declared uncertainty, and an optional corrupted observation.

04

Estimators

Solves inverse-variance WLS and iterative Huber robust weighting under the same inputs.

05

Evaluation

Calculates RMSE, maximum error, uncertainty coverage, interval width, conditioning, and bad-data localisation.

Methodology

Project workflow

  1. 01
    Select a case

    Choose light load, nominal, evening peak, or midday distributed-generation operation.

  2. 02
    Set meter coverage

    Use 10, 20, 35, or 55 percent voltage-meter coverage with the declared pseudo-measurement errors.

  3. 03
    Generate evidence

    The seeded simulator produces clean or single-corruption measurements for the selected repetition.

  4. 04
    Estimate the state

    WLS and Huber methods reconstruct the non-slack bus voltages and calculate uncertainty intervals.

  5. 05
    Compare results

    Retained CSV, JSON, figures, and the offline dashboard show accuracy, coverage, detection, and robustness.

Demonstration scenario

The evening-peak feeder is tested at 10, 20, 35, and 55 percent voltage-meter coverage. One direct voltage measurement is corrupted by 0.03 p.u. WLS and Huber estimates are compared using voltage error, uncertainty coverage, interval width, and largest-residual localisation.

Engineering

Tools and method

Tools
The project uses Python, pandapower, NumPy, SciPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Power flow
pandapower Newton-Raphson AC load flow supplies controlled truth for the balanced benchmark feeder.
Numerical model
Python and NumPy construct local voltage sensitivities from declared active-power and reactive-power perturbations.
Estimation
SciPy and NumPy solve weighted normal equations and Huber iterative reweighting.
Evidence
Pandas retains all 1,280 runs and Matplotlib creates eight labelled result figures.
Verification
Automated tests check feeder construction, measurements, estimation, metrics, experiment output, and command behaviour.

Testing

Evaluation

Evaluation measures

  • Mean and maximum voltage-state error under clean measurements
  • Voltage-state error after one corrupted direct measurement
  • Ninety-five percent uncertainty-interval coverage and mean interval width
  • Largest-residual bad-data localisation rate
  • Sensitivity to meter coverage and operating scenario
  • Sensitivity-matrix conditioning and estimator repeatability

Project boundaries

  • The project is a balanced static benchmark study and not an operational distribution management system.
  • Measurements, errors, and corruption are controlled synthetic evidence rather than utility field data.
  • Phase imbalance, switching and topology error, protection, communications, and control actions are outside this version.
  • A real deployment requires feeder-specific data, calibrated sensors, field validation, and review by qualified electrical engineers.

Included

  1. 01IEEE 33-bus radial distribution-feeder model
  2. 02AC power-flow truth cases and numerical voltage sensitivities
  3. 03Sparse voltage meters and uncertain pseudo measurements
  4. 04Weighted least squares and Huber robust estimators
  5. 05Clean and corrupted-meter experiments with uncertainty evaluation
  6. 061,280 retained runs, eight generated figures, and an offline dashboard
  7. 07Complete project files, calculations, tests, and results in a private GitHub repository
  8. 08Complete project documentation in PDF and editable Word formats
  9. 09Setup and usage guide in PDF and editable Word formats

Project record

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Permanent project ID
GP-EE-135KKNP
Catalogued
21 Aug 2026
Completed
24 Aug 2026
Verified
24 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.