ВИЯВЛЕННЯ «СЛАБКИХ» ПЕРЕТИНІВ В РЕЖИМІ ОПЕРАТИВНОГО КЕРУВАННЯ СТІЙКІСТЮ ЕНЕРГОСИСТЕМ

This article presents an approach for the fast online identification of weak interfaces in large transmission power systems (PS). Ensuring the stability of a PS is a critical challenge, especially due to the increasing complexity of interconnections and the growing demand for efficient energy transm...

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Бібліографічні деталі
Дата:2026
Автори: Павловський, В.В., Лук'яненко, Л.М., Гречко, В.В.
Формат: Стаття
Опубліковано: Інститут електродинаміки НАН України, Київ 2026
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Онлайн доступ:https://techned.org.ua/index.php/techned/article/view/1744
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Назва журналу:Technical Electrodynamics

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Technical Electrodynamics
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Резюме:This article presents an approach for the fast online identification of weak interfaces in large transmission power systems (PS). Ensuring the stability of a PS is a critical challenge, especially due to the increasing complexity of interconnections and the growing demand for efficient energy transmission. One of the key factors affecting system stability is the reliable operation of critical interfaces, which can be compromised due to excessive or unforeseen power flows. An in-depth analysis of weak interface identification are provided. The specific methodology that enables real-time monitoring and detection of weak interfaces, ensuring that power system operators can take timely corrective actions has been devel-oped. The proposed approach is based on analysing electrical distances, load distributions, and system stress points, allowing for improved control over power flow stability. Additionally, the study highlights the impact of weak interfaces on system reliability, emphasizing their role in preventing cascading failures. The findings contribute to the field of power system management, offering practical solutions for improving the reliability and operational efficiency of large-scale transmission grids. Future research directions may include further refinements of weak interface detection algorithms and the integration of artificial intelligence-based predictive models for enhanced grid stability assessment. References 6, tables 2, figures 5.