Open AccessStatic security assessment of power systems: A review
Author(s) -
Gholami Mostafa,
Sanjari Mohammad J.,
Safari Mostafa,
Akbari Mahdi,
Kamali Mohammadreza R.
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12432
Subject(s) - computer science , electric power system , feature selection , static analysis , data mining , network security , security controls , reliability engineering , power (physics) , machine learning , artificial intelligence , computer security , engineering , control (management) , physics , quantum mechanics , programming language
Summary The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.