Volume 21, Issue 4 (December 2025)                   IJEEE 2025, 21(4): 3860-3860 | Back to browse issues page

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Arabsadegh M, doroudi A. Resilience-Oriented Restoration After Storm-Induced Failures Using Genetic Algorithm and Probabilistic Circuit Breaker Modeling. IJEEE 2025; 21 (4) :3860-3860
URL: http://ijeee.iust.ac.ir/article-1-3860-en.html
Resilience-Oriented Restoration After Storm-Induced Failures Using Genetic Algorithm and Probabilistic Circuit Breaker Modeling
Mahdi Arabsadegh , Aref Doroudi
Abstract:   (84 Views)
This paper presents an advanced methodology for post-storm power system restoration. A real-time Condition Index (CI)-based classification scheme is introduced to categorize circuit breakers into high-reliability (Type A) and moderate-reliability (Type B) groups. Leveraging this classification, a genetic algorithm (GA) optimizes microgrid configurations to maximize power restoration probabilities by explicitly modeling the stochastic failure risks associated with circuit breakers under severe weather conditions. The approach was validated on the IEEE 118-bus system with five critical breakers deactivated due to storm conditions. The GA achieved a 92.5% load restoration after 200 iterations, surpassing a baseline Monte Carlo simulation that attained 85.2%. Computational efficiency was significantly improved, reducing execution time to approximately 15 minutes compared to 60 minutes for traditional methods, with enhanced accuracy indicated by a 1.8% error margin versus 7.5%. Key contributions include utilizing live CI data for dynamic breaker classification, which resulted in a 20% reduction in computational time, and demonstrating scalability and effectiveness on large-scale test systems such as the 118-bus network. The methodology's performance decreases to 78.3% load restoration when more than 14 breakers are compromised. Future research will focus on integrating detailed storm modeling—including wind speed profiles—and incorporating renewable energy resources to enhance grid resilience.
Keywords: resilience, Genetic Algorithm, Condition Index, probabilistic modeling, microgrids, storm-induced failures, load restoration.
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Type of Study: Research Paper | Subject: Power Systems Reliability
Received: 2025/04/07 | Revised: 2025/05/25 | Accepted: 2025/05/10
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.