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An adaptive power system transient stability assessment method based on shared feature extraction.

Jiexiang Hu1,2, Le Zheng2, Wei Ai3

  • 1Changsha Power Supply Branch, State Grid Hunan Electric Power Co., Ltd., Changsha 410035, China.

Iscience
|April 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive power system transient stability assessment (TSA) method using shared feature extraction. It maintains high accuracy despite data loss and changing scenarios, improving robustness and generalizability.

Keywords:
Energy systemsEngineering

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Power system transient stability assessment (TSA) using machine learning struggles with performance degradation in dynamic operating conditions.
  • Existing methods often require extensive labeled data and lack adaptability to evolving system scenarios.

Purpose of the Study:

  • To develop a robust and transferable adaptive TSA method for power systems.
  • To enhance the generalizability and sustainability of machine learning models for TSA across varying operational contexts.

Main Methods:

  • Utilized a domain adversarial alignment network to train a shared feature extractor.
  • Aligned power system data from before and after operational variations to capture critical stability features.
  • Implemented simultaneous data and model knowledge transfer for continuous learning.

Main Results:

  • Achieved over 96% prediction accuracy with 30% data loss on the IEEE 39-bus and a 2179-node system.
  • Sustained 97.99% accuracy in continuously changing scenarios, outperforming traditional methods.
  • Demonstrated reduced reliance on extensive labeled data for new scenarios.

Conclusions:

  • The proposed adaptive TSA method offers enhanced generalizability and robustness for real-world power system applications.
  • The approach enables sustainable learning capabilities, adapting effectively to system variations and data scarcity.
  • This method presents a significant advancement for reliable and efficient power system stability monitoring.