| dc.contributor.author | Ozgonenel O. | |
| dc.contributor.author | Thomas D.W.P. | |
| dc.contributor.author | Yalcin T. | |
| dc.contributor.author | Bertizlioglu I.N. | |
| dc.date.accessioned | 2020-06-21T09:28:59Z | |
| dc.date.available | 2020-06-21T09:28:59Z | |
| dc.date.issued | 2012 | |
| dc.identifier.isbn | 9.78185E+12 | |
| dc.identifier.uri | https://doi.org/10.1049/cp.2012.0079 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12712/4409 | |
| dc.description | 11th IET International Conference on Developments in Power Systems Protection, DPSP 2012 -- 23 April 2012 through 26 April 2012 -- Birmingham -- 91064 | en_US |
| dc.description.abstract | This paper presents a novel approach for the detection of abnormal power system states that force systems into blackout. K-means clustering techniques and two types of distances for identifying pattern clusters are used to detect abnormal conditions. PCA is used for the reduction of the data matrix for faster calculations. The proposed hybrid technique is then demonstrated on an IEEE 14-bus system. | en_US |
| dc.language.iso | eng | en_US |
| dc.relation.isversionof | 10.1049/cp.2012.0079 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Anomaly (Outlier) Detection | en_US |
| dc.subject | Blackout | en_US |
| dc.subject | K-Means Clustering | en_US |
| dc.subject | Principal Component Analysis (PCA) | en_US |
| dc.title | Detection of blackouts by using K-means clustering in a power system | en_US |
| dc.type | conferenceObject | en_US |
| dc.contributor.department | OMÜ | en_US |
| dc.identifier.volume | 2012 | en_US |
| dc.identifier.issue | 593 CP | en_US |
| dc.relation.journal | IET Conference Publications | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
