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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.
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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.
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Parameter Identification of Permanent Magnet Synchronous Motor Based on LSOSMO Algorithm.

Songcan Zhang1, Zhuangzhuang Zhou1, Yi Pu2

  • 1College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new LSOSMO algorithm for identifying Permanent Magnet Synchronous Motor (PMSM) parameters, improving accuracy and stability in servo system control. The enhanced method achieves identification errors below 1.1%.

Keywords:
PMSMsadaptive t-distribution methodchaotic mappingopposition-based learning strategyparameter identificationspider monkey optimization algorithm

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Optimization Algorithms

Background:

  • Accurate parameter identification is crucial for optimal performance of Permanent Magnet Synchronous Motors (PMSMs) in servo systems.
  • Existing methods like the spider monkey optimization (SMO) algorithm face challenges including slow convergence, local optima, and unstable results.
  • Key PMSM parameters requiring precise identification include stator resistance (R), dq-axis inductance (Ld, Lq), and flux linkage (ψf).

Purpose of the Study:

  • To develop a novel and improved algorithm for the precise identification of PMSM electric parameters.
  • To address the limitations of existing algorithms, specifically the slow identification speed and tendency to fall into local optima.
  • To enhance the stability and accuracy of parameter identification for better servo system control.

Main Methods:

  • The proposed LSOSMO algorithm integrates logistic-sine chaotic mapping, dynamic probability adaptive T-distribution, and opposition-based learning.
  • Logistic-sine chaotic mapping is employed to improve the initial population's uniformity in the SMO algorithm.
  • Dynamic probability adaptive T-distribution and opposition-based learning replace greedy strategies to enhance global and local search capabilities, improving convergence speed.

Main Results:

  • The LSOSMO algorithm demonstrated superior stability and accuracy in identifying PMSM parameters compared to five other algorithms.
  • Identification errors for the four key parameters (R, Ld, Lq, ψf) were consistently below 1.1% relative to true values.
  • The enhanced algorithm showed improved performance and convergence speed, validating its effectiveness.

Conclusions:

  • The LSOSMO algorithm offers a reliable and effective solution for accurate PMSM parameter identification.
  • The integration of chaotic mapping, adaptive T-distribution, and opposition-based learning significantly enhances optimization performance.
  • The findings confirm the algorithm's potential for improving the control performance of servo systems reliant on PMSMs.