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Enhancing condition monitoring of distributed generation systems through optimal sensor selection

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Enhancing condition monitoring of distributed generation systems through optimal sensor selection. / Wang, Yifei; Ma, Xiandong; Joyce, Malcolm.
Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE. Piscataway, N.J.: IEEE, 2013. p. 7610-7616.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Wang, Y, Ma, X & Joyce, M 2013, Enhancing condition monitoring of distributed generation systems through optimal sensor selection. in Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE. IEEE, Piscataway, N.J., pp. 7610-7616, Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IEEE IECON 2013), Vienna, Austria, 10/11/13. https://doi.org/10.1109/IECON.2013.6700401

APA

Wang, Y., Ma, X., & Joyce, M. (2013). Enhancing condition monitoring of distributed generation systems through optimal sensor selection. In Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE (pp. 7610-7616). IEEE. https://doi.org/10.1109/IECON.2013.6700401

Vancouver

Wang Y, Ma X, Joyce M. Enhancing condition monitoring of distributed generation systems through optimal sensor selection. In Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE. Piscataway, N.J.: IEEE. 2013. p. 7610-7616 doi: 10.1109/IECON.2013.6700401

Author

Wang, Yifei ; Ma, Xiandong ; Joyce, Malcolm. / Enhancing condition monitoring of distributed generation systems through optimal sensor selection. Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE. Piscataway, N.J. : IEEE, 2013. pp. 7610-7616

Bibtex

@inproceedings{61315d6808f441f78502ca123005bd8f,
title = "Enhancing condition monitoring of distributed generation systems through optimal sensor selection",
abstract = "Distributed generation (DG) systems comprising of renewable energy generation technologies will play a significantly increasing role for future power systems. One of the key concerns for deployment of DG systems is specifically related to their availability and reliability, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by huge amount of data to be processed especially in real time in order to reveal healthy conditions of the system. In this paper, an optimal sensor selection method based on principal component analysis (PCA) is proposed for condition monitoring of a DG system oriented to wind turbines. The proposed method is examined with both simulation data from PSCAD/EMTDC and SCADA data of an operational wind farm in the time, frequency, and time-frequency domains. The results have shown that the proposed technique could reduce the number of sensors whilst still maintaining sufficient information to assess the system's conditions.",
author = "Yifei Wang and Xiandong Ma and Malcolm Joyce",
year = "2013",
doi = "10.1109/IECON.2013.6700401",
language = "English",
isbn = "9781479902248",
pages = "7610--7616",
booktitle = "Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE",
publisher = "IEEE",
note = "Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IEEE IECON 2013) ; Conference date: 10-11-2013 Through 13-11-2013",

}

RIS

TY - GEN

T1 - Enhancing condition monitoring of distributed generation systems through optimal sensor selection

AU - Wang, Yifei

AU - Ma, Xiandong

AU - Joyce, Malcolm

PY - 2013

Y1 - 2013

N2 - Distributed generation (DG) systems comprising of renewable energy generation technologies will play a significantly increasing role for future power systems. One of the key concerns for deployment of DG systems is specifically related to their availability and reliability, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by huge amount of data to be processed especially in real time in order to reveal healthy conditions of the system. In this paper, an optimal sensor selection method based on principal component analysis (PCA) is proposed for condition monitoring of a DG system oriented to wind turbines. The proposed method is examined with both simulation data from PSCAD/EMTDC and SCADA data of an operational wind farm in the time, frequency, and time-frequency domains. The results have shown that the proposed technique could reduce the number of sensors whilst still maintaining sufficient information to assess the system's conditions.

AB - Distributed generation (DG) systems comprising of renewable energy generation technologies will play a significantly increasing role for future power systems. One of the key concerns for deployment of DG systems is specifically related to their availability and reliability, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by huge amount of data to be processed especially in real time in order to reveal healthy conditions of the system. In this paper, an optimal sensor selection method based on principal component analysis (PCA) is proposed for condition monitoring of a DG system oriented to wind turbines. The proposed method is examined with both simulation data from PSCAD/EMTDC and SCADA data of an operational wind farm in the time, frequency, and time-frequency domains. The results have shown that the proposed technique could reduce the number of sensors whilst still maintaining sufficient information to assess the system's conditions.

U2 - 10.1109/IECON.2013.6700401

DO - 10.1109/IECON.2013.6700401

M3 - Conference contribution/Paper

SN - 9781479902248

SP - 7610

EP - 7616

BT - Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE

PB - IEEE

CY - Piscataway, N.J.

T2 - Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IEEE IECON 2013)

Y2 - 10 November 2013 through 13 November 2013

ER -