Predictive Analytics for Hydropower Fleet Intelligence

Yigit Yucesan; Pradeep Ramuhalli; Yang Chen; Jim Miller; Edward Hanson; Stephen Signore

Predictive Analytics for Hydropower Fleet Intelligence

Yigit Yucesan, Pradeep Ramuhalli, Yang Chen, Jim Miller, Edward Hanson, Stephen Signore

Modern Nuclear I&C

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

Abstract

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Original language	English
Title of host publication	Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Editors	Chetan S. Kulkarni, Indranil Roychoudhury
Publisher	Prognostics and Health Management Society
Edition	1
ISBN (Electronic)	9781936263059
State	Published - 2023
Event	15th Annual Conference of the Prognostics and Health Management Society, PHM 2023 - Salt Lake City, United States Duration: Oct 28 2023 → Nov 2 2023

Publication series

Name	Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number	1
Volume	15
ISSN (Print)	2325-0178

Conference

Conference	15th Annual Conference of the Prognostics and Health Management Society, PHM 2023
Country/Territory	United States
City	Salt Lake City
Period	10/28/23 → 11/2/23

Funding

Yucesan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work was supported by the US DOE Water Power Technologies Office and conducted at Oak Ridge National Laboratory. The authors are employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US DOE. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes.

Funders	Funder number
U.S. Department of Energy
Water Power Technologies Office	DE-AC05-00OR22725

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Yucesan, Y., Ramuhalli, P., Chen, Y., Miller, J., Hanson, E., & Signore, S. (2023). Predictive Analytics for Hydropower Fleet Intelligence. In C. S. Kulkarni, & I. Roychoudhury (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 ed.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 15, No. 1). Prognostics and Health Management Society.

Yucesan, Yigit ; Ramuhalli, Pradeep ; Chen, Yang et al. / Predictive Analytics for Hydropower Fleet Intelligence. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. editor / Chetan S. Kulkarni ; Indranil Roychoudhury. 1. ed. Prognostics and Health Management Society, 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1).

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title = "Predictive Analytics for Hydropower Fleet Intelligence",

abstract = "A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.",

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Yucesan, Y, Ramuhalli, P , Chen, Y, Miller, J, Hanson, E & Signore, S 2023, Predictive Analytics for Hydropower Fleet Intelligence. in CS Kulkarni & I Roychoudhury (eds), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 edn, Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, no. 1, vol. 15, Prognostics and Health Management Society, 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023, Salt Lake City, United States, 10/28/23.

Predictive Analytics for Hydropower Fleet Intelligence. / Yucesan, Yigit; Ramuhalli, Pradeep ; Chen, Yang et al.
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. ed. / Chetan S. Kulkarni; Indranil Roychoudhury. 1. ed. Prognostics and Health Management Society, 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 15, No. 1).

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

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AU - Ramuhalli, Pradeep

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AU - Hanson, Edward

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N2 - A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

AB - A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

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M3 - Conference contribution

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T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM

BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM

A2 - Kulkarni, Chetan S.

A2 - Roychoudhury, Indranil

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Yucesan Y, Ramuhalli P , Chen Y, Miller J, Hanson E, Signore S. Predictive Analytics for Hydropower Fleet Intelligence. In Kulkarni CS, Roychoudhury I, editors, Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 ed. Prognostics and Health Management Society. 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1).

Predictive Analytics for Hydropower Fleet Intelligence

Abstract

Publication series

Conference

Funding

UN SDGs

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