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Equal-area criterion in power systems revisited
Author(s) -
Yong Sun,
Jinpeng Ma,
Jürgen Kurths,
Meng Zhan
Publication year - 2018
Publication title -
proceedings of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2946
pISSN - 1364-5021
DOI - 10.1098/rspa.2017.0733
Subject(s) - transient (computer programming) , bipartite graph , stability (learning theory) , electric power system , clearance , computer science , power (physics) , fault (geology) , realization (probability) , key (lock) , control theory (sociology) , mathematics , theoretical computer science , artificial intelligence , physics , statistics , machine learning , programming language , medicine , graph , computer security , control (management) , quantum mechanics , seismology , urology , geology
The classic equal-area criterion (EAC) is of key importance in power system analysis, and provides a powerful, pictorial and quantitative means of analysing transient stability (i.e. the system's ability to maintain stable operation when subjected to a large disturbance). Based on the traditional EAC, it is common sense in engineering that there is a critical cleaning time (CCT); namely, a power system is stable (unstable) if a fault is cleared before (after) this CCT. We regard this form of CCT as bipartite. In this paper, we revisit the EAC theory and, surprisingly, find different kinds of transient stability behaviour. Based on these analyses, we discover that the bipartite CCT is only one type among four major types, and, actually, the forms of CCT can be diversified. In particular, under some circumstances, a system may have no CCT or show a periodic CCT. Our theoretical analysis is verified by numerical simulations in a single-machine-infinite-bus system and also in multi-machine systems. Thus, our study provides a panoramic framework for diverse transient stability behaviour in power systems and also may have a significant impact on applications of multi-stability in various other systems, such as neuroscience, climatology or photonics.

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