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

Xueqin Liu; David Laverty; Robert Best

doi:10.1109/PESGM.2014.6939272

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

Xueqin Liu, David Laverty, Robert Best

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2014 IEEE, PES General Meeting - Conference & Exposition
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781479964154
DOIs	https://doi.org/10.1109/PESGM.2014.6939272
Publication status	Published - 27 Jul 2014
Event	IEEE Power & Energy Society General Meeting - Colorado, Denver, United States Duration: 27 Jul 2015 → 31 Jul 2015 http://www.pes-gm.org/2015/

Conference

Conference	IEEE Power & Energy Society General Meeting
Country	United States
City	Denver
Period	27/07/2015 → 31/07/2015
Internet address	http://www.pes-gm.org/2015/

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10.1109/PESGM.2014.6939272

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1 Participation in conference

IEEE PES General Meeting 2014
Xueqin Liu (Participant)
2014
Activity: Participating in or organising an event types › Participation in conference

Cite this

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title = "Islanding Detection Based on Probabilistic PCA with Missing Values in PMU Data",

abstract = "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.",

author = "Xueqin Liu and David Laverty and Robert Best",

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doi = "10.1109/PESGM.2014.6939272",

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AU - Laverty, David

AU - Best, Robert

PY - 2014/7/27

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N2 - 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.

AB - 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.

U2 - 10.1109/PESGM.2014.6939272

DO - 10.1109/PESGM.2014.6939272

M3 - Conference contribution

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BT - 2014 IEEE, PES General Meeting - Conference & Exposition

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IEEE Power & Energy Society General Meeting

Y2 - 27 July 2015 through 31 July 2015

ER -

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

Abstract

Conference

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IEEE PES General Meeting 2014

Cite this