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With a portfolio of 56 hydroelectric plants, CPFL Energia, one of the largest companies in the electrical sector in Brazil and controlled by the Chinese State Grid, implemented an innovative solution to optimize the monitoring and safety of its dams. The initiative combines data analytics and machine learning to analyze data from more than 1,800 auscultation instruments, enabling early detection of anomalies and taking preventative measures. The solution involves the use of techniques such as Statistical Quality Control, Prophet model. One Class Support Vector Machine and Isolation Forest to identify abnormal readings and map risk scenarios in each block or section of the dam. This proactive approach contributes to providing safety, efficient asset management and the generation of clean, renewable energy.

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Hydro 4.0 – the Digital Dam Safety Platform of CPFL Energia Group – Brazil

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Arthur Couto Mantese,
  • Xinjian Chen,
  • Raphael Moncacci Borges da Silva,
  • Camila Gomes,
  • Luis Fernando Pedrozo Melegari,
  • Huiyi Zhang

摘要

With a portfolio of 56 hydroelectric plants, CPFL Energia, one of the largest companies in the electrical sector in Brazil and controlled by the Chinese State Grid, implemented an innovative solution to optimize the monitoring and safety of its dams. The initiative combines data analytics and machine learning to analyze data from more than 1,800 auscultation instruments, enabling early detection of anomalies and taking preventative measures. The solution involves the use of techniques such as Statistical Quality Control, Prophet model. One Class Support Vector Machine and Isolation Forest to identify abnormal readings and map risk scenarios in each block or section of the dam. This proactive approach contributes to providing safety, efficient asset management and the generation of clean, renewable energy.