Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

Yuanjun Guo, Kang Li, D. M. Laverty

Research output: Contribution to journalArticlepeer-review

353 Downloads (Pure)


In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.
Original languageEnglish
Pages (from-to)423-431
Number of pages9
JournalJournal of Power and Energy Engineering
Issue number4
Publication statusPublished - Apr 2014


Dive into the research topics of 'Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis'. Together they form a unique fingerprint.

Cite this