Parallel-differential evolution approach for optimal event-driven load shedding against voltage collapse in power systems
Event-driven load shedding is an effective countermeasure against voltage collapse in power systems. Conventionally, its optimisation relies on sensitivity-based linear methods, which, however, could suffer from unrealistic assumptions and sub-optimality. In this study, an alternative approach based...
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Published in | IET generation, transmission & distribution Vol. 8; no. 4; pp. 651 - 660 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Stevenage
The Institution of Engineering and Technology
01.04.2014
Institution of Engineering and Technology The Institution of Engineering & Technology |
Subjects | |
Online Access | Get full text |
ISSN | 1751-8687 1751-8695 |
DOI | 10.1049/iet-gtd.2013.0385 |
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Summary: | Event-driven load shedding is an effective countermeasure against voltage collapse in power systems. Conventionally, its optimisation relies on sensitivity-based linear methods, which, however, could suffer from unrealistic assumptions and sub-optimality. In this study, an alternative approach based on parallel-differential evolution (P-DE) is proposed for efficiently and globally optimising the event-driven load shedding against voltage collapse. Working in a parallel structure, the approach consists of candidate buses selection, voltage stability assessment (VSA) and DE optimisation. Compared with conventional methods, it fully considers the non-linearity of the problem and is able to effectively escape from local optima and not limited to system modelling and unrealistic assumptions. Besides, any type of objective functions and VSA techniques can be used. The proposed approach has been tested on the IEEE 118-bus test system considering two cases for preventive control and corrective control, respectively, and compared with the two existing methods. Simulation results have verified its effectiveness and superiority over the compared methods. |
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Bibliography: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 1751-8687 1751-8695 |
DOI: | 10.1049/iet-gtd.2013.0385 |