Islanding Detection Based on Probabilistic PCA with Missing Values in PMU Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)


This paper proposes a probabilistic principal component analysis (PCA) approach applied to islanding detection study based on wide area PMU data. The increasing probability of uncontrolled islanding operation, according to many power system operators, is one of the biggest concerns with a large penetration of distributed renewable generation. The traditional islanding detection methods, such as RoCoF and vector shift, are however extremely sensitive and may result in many unwanted trips. The proposed probabilistic PCA aims to improve islanding detection accuracy and reduce the risk of unwanted tripping based on PMU measurements, while addressing a practical issue on missing data. The reliability and accuracy of the proposed probabilistic PCA approach are demonstrated using real data recorded in the UK power system by the OpenPMU project. The results show that the proposed methods can detect islanding accurately, without being falsely triggered by generation trips, even in the presence of missing values.
Original languageEnglish
Title of host publication2014 IEEE, PES General Meeting - Conference & Exposition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479964154
Publication statusPublished - 27 Jul 2014
EventIEEE Power & Energy Society General Meeting - Colorado, Denver, United States
Duration: 27 Jul 201531 Jul 2015


ConferenceIEEE Power & Energy Society General Meeting
Country/TerritoryUnited States
Internet address


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