Open AccessA machine learning approach for real‐time selection of preventive actions improving power network resilience
Author(s) -
Noebels Matthias,
Preece Robin,
Panteli Mathaios
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12287
Subject(s) - computer science , process (computing) , islanding , cascading failure , event (particle physics) , extreme learning machine , resilience (materials science) , selection (genetic algorithm) , reliability engineering , control reconfiguration , network topology , electric power system , machine learning , artificial intelligence , power (physics) , risk analysis (engineering) , distributed computing , artificial neural network , engineering , computer network , embedded system , physics , quantum mechanics , thermodynamics , operating system , medicine
Power outages due to cascading failures which are triggered by extreme weather pose an increasing risk to modern societies and draw attention to an emerging need for power network resilience. Machine learning (ML) is used for a real‐time selection process on preventive actions, such as topology reconfiguration and islanding, aiming to reduce the risk of cascading failures. Training data is obtained from Monte Carlo simulations of cascading failures triggered by extreme events. The trained ML‐based decision‐making process uses only predictors that are readily available prior to an extreme event, such as event location and intensity, network topology and load, and requires no further time‐consuming simulations.The proposed decision‐making process is compared to time‐consuming but ideal decision‐making and fast but trivial decision‐making. Demonstrations on the German transmission network show that the proposed ML‐based selection process efficiently prevents the uncontrolled propagation of cascading failures and performs similarly to an ideal decision‐making process whilst being computationally three orders of magnitude faster.