Predictive Analytics for Hydropower Fleet Intelligence
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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.
How to Cite
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Hydropower, Reliability, Anomaly Detection
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Kuo, W., & Zuo, M. J. (2003). Optimal reliability modeling: principles and applications. John Wiley & Sons. Lins, H. F. (2012). Usgs hydro-climatic data network 2009 (hcdn-2009). US Geological Survey Fact Sheet, 3047(4).
Martinez, R., Johnson, M., & Shan, R. (2021, January). U.s. hydropower market report (january 2021 edition) (Tech. Rep.). doi: 10.2172/1763453
Martz, H. F. (2003). Reliability theory. Elsevier. Mohanta, R. K., Chelliah, T. R., Allamsetty, S., Akula, A., & Ghosh, R. (2017, April). Sources of vibration and their treatment in hydro power stations-a review. Engineering Science and Technology, an International Journal, 20(2), 637–648. doi: 10.1016/j.jestch.2016.11.004
Mu, B., Peng, C., Yuan, S., & Chen, L. (2019, July). ENSO forecasting over multiple time horizons using ConvLSTM network and rolling mechanism. In 2019 international joint conference on neural networks (IJCNN). IEEE. doi: 10.1109/ijcnn.2019.8851967
Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019). Multi-output bus travel time prediction with convolutional
lstm neural network. doi: 10.48550/ARXIV.1903.02791
Ramasamy, V., Zuboy, J., O'Shaughnessy, E., Feldman, D., Desai, J., Woodhouse, M., Margolis, R. (2022, September). U.s. solar photovoltaic system and energy storage cost benchmarks, with minimum sustainable price analysis: Q1 2022 (Tech. Rep.). Retrieved from https://doi.org/10.2172/1891204 doi: 10.2172/1891204
Reid, N., & Cox, D. (2018). Analysis of survival data. Chapman and Hall/CRC. Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon,
G., Samek, W., Kloft, M., . . . Muller, K.-R. (2021, May). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795. doi: 10.1109/jproc.2021.3052449
Sapitang, M., Ridwan, W. M., Kushiar, K. F., Ahmed, A. N., & El-Shafie, A. (2020, July). Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy. Sustainability, 12(15), 6121. Retrieved from
https://doi.org/10.3390/su12156121 doi: 10.3390/su12156121
Smith, B., Smyth, S., Peterson, A., Ramuhalli, P., Hu, I., & Bulmer, A. (2022, April). Consolidated hydropower
data repository: Value and opportunities (Tech. Rep.). doi: 10.2172/1870208
Smith, B. T., Samu, N., Curd, S. L.,Wei, Y., &Wei, Z. (2019, 3). Hydrosource data dictionary. doi: 10.2172/1619046
Stehly, T., & Duffy, P. (2022, December). 2021 cost of wind energy review (Tech. Rep.). Retrieved from https://doi.org/10.2172/1907623 doi:10.2172/1907623
Sun, J., Wang, X., Xiong, N., & Shao, J. (2018). Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access, 6, 33353–33361. doi: 10.1109/access.2018.2848210
Tayeh, T., Aburakhia, S., Myers, R., & Shami, A. (2022, April). An attention-based ConvLSTM autoencoder with dynamic thresholding for unsupervised anomaly detection in multivariate time series. Machine Learning and Knowledge
Extraction, 4(2), 350–370. Retrieved from https://doi.org/10.3390/make4020015 doi: 10.3390/make4020015
Tubeuf, C., Birkelbach, F., Maly, A., & Hofmann, R. (2023, February). Increasing the flexibility of hydropower with reinforcement learning on a digital twin platform. Energies, 16(4), 1796. Retrieved from https://doi.org/10.3390/en16041796
doi: 10.3390/en16041796
Water power technologies office: Multi-year program plan. (2022, 3). doi: 10.2172/1863492
Xiao, Y., Yin, H., Zhang, Y., Qi, H., Zhang, Y., & Liu, Z. (2021, January). A dual-stage attentionbased conv-LSTM network for spatio-temporal correlation and multivariate time series prediction. International Journal of Intelligent Systems, 36(5), 2036–2057. Retrieved from https://doi.org/10.1002/int.22370 doi: 10.1002/int.22370 12
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