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A Data-Driven Framework for Small Hydroelectric Plant Prognosis Using Tsfresh and Machine Learning Survival Models.

Rodrigo Barbosa de Santis1, Tiago Silveira Gontijo1, Marcelo Azevedo Costa1,2

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Predicting failures in small hydroelectric plants (SHPs) is vital for clean energy. This study introduces a data-driven approach using time series analysis and survival modeling to improve fault prognosis and reduce operational costs.

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Area of Science:

  • Engineering
  • Renewable Energy Systems
  • Data Science

Background:

  • Small hydroelectric plants (SHPs) are crucial for expanding clean energy, but require effective maintenance strategies.
  • Accurate fault prognosis in SHPs is essential for optimizing asset management, reducing operational costs, and ensuring reliable energy supply.
  • Existing fault prognosis models often struggle with censored data common in SHPs due to operational interruptions.

Purpose of the Study:

  • To develop and evaluate a novel data-driven framework for fault prognosis in small hydroelectric plants.
  • To address the challenge of censored data in SHP operational records.
  • To compare different feature engineering strategies and machine learning survival models for improved prognosis accuracy.

Main Methods:

  • A two-step framework combining time series feature engineering and survival modeling was proposed.
  • Two feature engineering strategies were compared: higher-order statistics and the Tsfresh algorithm.
  • Three machine learning survival models (CoxNet, survival random forests, gradient boosting survival analysis) were implemented and evaluated using the concordance index.

Main Results:

  • The proposed framework achieved a significant concordance index of 77.44% with the best-performing model.
  • Higher-order statistics (kurtosis, variance) and Tsfresh transformations (fast Fourier transform, continuous wavelet transform) were identified as effective feature extraction methods.
  • Temperature sensors at the generator bearing, hydraulic unit oil, and turbine radial bushing were found to be the most critical for monitoring.

Conclusions:

  • The developed data-driven framework effectively improves fault prognosis in small hydroelectric plants, even with censored data.
  • Feature engineering and survival modeling offer a robust approach to predicting failures and enhancing maintenance strategies for SHPs.
  • Monitoring specific temperature sensors provides valuable insights for early fault detection in SHP operations.