Early Prevention of Instability-Use of Self Propagating Graph for the Fast Search for Optimal Grid Nodes to Apply Countermeasures

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2013

View graph of relations

This paper presents a method for a fast determination of the grid nodes where countermeasures, in the form of changes in nodal admittance, would provide greatest impact on the stability margin for a specific generator that is facing the risk of instability. The sensitivity of the stability criteria for aperiodic
small signal angular stability to the change in nodal admittance is used as a factor quantifying impact that the node has on the stability of a critical generator. In order to lower the number of nodes which are processed through sensitivity analysis, a selfpropagating graph with discrete steps is applied. The suggested
method is tested on the IEEE 30 bus test system and on the 1648 bus US west coast test system where the results show that the number of nodes processed through sensitivity analysis are well reduced compared to the full sensitivity analysis, illustrating the potential of the developed approach for the fast identification of the optimal nodes for countermeasure application.
Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE PowerTech Conference
Number of pages5
PublisherIEEE
Publication date2013
DOIs
StatePublished - 2013
EventIEEE PowerTech Conference - Grenoble, France
Duration: 16 Jun 201320 Jun 2013

Conference

ConferenceIEEE PowerTech Conference
CountryFrance
CityGrenoble
Period16/06/201320/06/2013
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Graph theory, Power systems, Sensitivity analysis, Stability
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 57802986