Predictive Analytics for Hydropower Fleet Intelligence



Published Oct 26, 2023
Yigit Yucesan Pradeep Ramuhalli Yang Chen Jim Miller Edward Hanson Stephen Signore


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.

How to Cite

Yucesan, Y., Ramuhalli, P., Chen, Y., Miller, J., Hanson, E., & Signore, S. (2023). Predictive Analytics for Hydropower Fleet Intelligence. Annual Conference of the PHM Society, 15(1).
Abstract 210 | PDF Downloads 128



Hydropower, Reliability, Anomaly Detection

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