Symmetry Studies and Application in Power System Stability

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 450

Special Issue Editors


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Guest Editor
Department of Energy Technology, Aalborg University, Aalborg, Denmark
Interests: control, modeling, and stability analysis of power electronics-based systems

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Guest Editor
Department of Electrical Engineering, School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
Interests: renewable energy power systems modeling and control; power electronics dominated power systems stability and control; virtual synchronous generator

Special Issue Information

Dear Colleagues,

The dynamic performance, stability, and reliability of power systems is closely related to symmetry, which manifests itself in system topology, components, and layout. This Special Issue provides a good platform for leading researchers worldwide to provide valuable insights into the symmetry/asymmetry of power systems with high-quality results, highlighting symmetry/asymmetry’s impact and application in the system stability. Various related aspects will be covered, including modeling and analysis methodologies, system design and operation, fault diagnosis, and signal processing.

This Special Issue invites researchers to contribute original research and review articles related to symmetry and its impact and application on power system stability. The topics of interest for this Special Issue include, but are not limited to, the following:

  1. Critical questions and the fundamental theory of the symmetry/asymmetry of power systems;
  2. Analysis methodology for the symmetry/asymmetry of power system (including renewable energy systems);
  3. Treatment and modeling considering the symmetric and asymmetric components, layout, and topology of power systems;
  4. Fault diagnosis for power systems with symmetrical/asymmetrical topology;
  5. Analysis and processing methods of symmetrical and asymmetrical electrical signals;
  6. Symmetry and balance of reactive and active power in power systems;
  7. Symmetry/asymmetry of power flow and transmission;
  8. Interaction or coupling caused by asymmetry of power systems;
  9. Stability of power electronics-dominated power systems with symmetries/asymmetries;
  10. Symmetry in multiphase power systems and power synchronization.

We look forward to receiving your contributions.

Dr. Siqi Wu
Dr. Chang Li
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry
  • power system
  • power electronics
  • symmetric fault
  • asymmetric fault
  • stability
  • balanced condition
  • renewable energy
  • signal processing
  • inverter-based resources

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Published Papers (1 paper)

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Research

22 pages, 1000 KiB  
Article
A Transfer-Learning-Based Approach to Symmetry-Preserving Dynamic Equivalent Modeling of Large Power Systems with Small Variations in Operating Conditions
by Lahiru Aththanayake, Devinder Kaur, Shama Naz Islam, Ameen Gargoom and Nasser Hosseinzadeh
Symmetry 2025, 17(7), 1023; https://doi.org/10.3390/sym17071023 - 29 Jun 2025
Viewed by 136
Abstract
Robust dynamic equivalents of large power networks are essential for fast and reliable stability analysis of bulk power systems. This is because the dimensionality of modern power systems raises convergence issues in modern stability-analysis programs. However, even with modern computational power, it is [...] Read more.
Robust dynamic equivalents of large power networks are essential for fast and reliable stability analysis of bulk power systems. This is because the dimensionality of modern power systems raises convergence issues in modern stability-analysis programs. However, even with modern computational power, it is challenging to find reduced-order models for power systems due to the following factors: the tedious mathematical analysis involved in the classical reduction techniques requires large amounts of computational power; inadequate information sharing between geographical areas prohibits the execution of model-dependent reduction techniques; and frequent fluctuations in the operating conditions (OPs) of power systems necessitate updates to reduced models. This paper focuses on a measurement-based approach that uses a deep artificial neural network (DNN) to estimate the dynamics of an external system (ES) of a power system, enabling stability analysis of a study system (SS). This DNN technique requires boundary measurements only between the SS and the ES. However, machine learning-based techniques like this DNN are known for their extensive training requirements. In particular, for power systems that undergo continuous fluctuations in operating conditions due to the use of renewable energy sources, the applications of this DNN technique are limited. To address this issue, a Deep Transfer Learning (DTL)-based technique is proposed in this paper. This approach accounts for variations in the OPs such as time-to-time variations in loads and intermittent power generation from wind and solar energy sources. The proposed technique adjusts the parameters of a pretrained DNN model to a new OP, leveraging symmetry in the balanced adaptation of model layers to maintain consistent dynamics across operating conditions. The experimental results were obtained by representing the Queensland (QLD) system in the simplified Australian 14 generator (AU14G) model as the SS and the rest of AU14G as the ES in five scenarios that represent changes to the OP caused by variations in loads and power generation. Full article
(This article belongs to the Special Issue Symmetry Studies and Application in Power System Stability)
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