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Related Concept Videos

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
924
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Multimachine Stability01:25

Multimachine Stability

629
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
629
Generator Voltage Control01:21

Generator Voltage Control

821
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand, use...
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Control of Power Flow01:30

Control of Power Flow

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There are several methods to control power flow in power systems:
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Related Experiment Video

Updated: Apr 15, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Data-Driven Parameter Identification of Synchronous Generators: A Three-Stage Framework with State Consistency and

Rasool Peykarporsan1, Tharuka Govinda Waduge1, Tek Tjing Lie1

  • 1Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

This study introduces a data-driven framework for identifying synchronous generator parameters using Port-Hamiltonian (PH) models. The method enhances power system stability analysis by overcoming limitations of existing techniques.

Keywords:
Lyapunov stabilitydata-driven identificationenergy-consistent modelingparameter identificationphysics-based regularizationport-Hamiltonian systemspower system dynamicssynchronous generator modeling

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Area of Science:

  • Electrical Engineering
  • Control Theory
  • Power Systems

Background:

  • Modern power systems require advanced stability analysis for complex nonlinear dynamics.
  • Port-Hamiltonian (PH) frameworks offer stable, energy-consistent, and scalable models for power systems.
  • Lack of reliable PH-compatible parameter identification from sensor data hinders practical PH stability analysis.

Purpose of the Study:

  • To propose a three-stage data-driven framework for identifying synchronous generator parameters within a Port-Hamiltonian (PH) model structure.
  • To address limitations of the IEEE Standard 115 for PH modeling, including single-scenario constraints and noise sensitivity.
  • To enable accurate parameter identification from sensor measurements for enhanced power system stability analysis.

Main Methods:

  • A three-stage data-driven identification framework utilizing multi-scenario excitation with sensor-acquired voltage and current signals.
  • Derivative-free state consistency optimization to avoid noise amplification from sensor measurements.
  • Physics-based regularization to ensure adherence to the PH model structure during parameter identification.

Main Results:

  • Complete identification of eight key synchronous generator parameters (H, D, Xd, Xq, Xd', Xq', Tdo', Tqo') with identification errors between 1.26% and 9.10%.
  • Validation demonstrated Root Mean Square (RMS) rotor angle errors below 1.2° and speed errors below 0.15%.
  • The framework successfully decouples generator-internal damping from grid contributions, a limitation of previous methods.

Conclusions:

  • The proposed data-driven framework accurately identifies synchronous generator parameters compatible with Port-Hamiltonian models.
  • This advancement supports reliable transient stability analysis, passivity-based control design, and oscillation damping assessment in complex power systems.
  • The method overcomes key limitations of traditional parameter identification techniques, paving the way for wider PH model adoption.