ENSIGHTS: Intelligent Monitoring of Electric Power Transmission Assets


  • Alex de Vasconcellos Garcia
  • Gabriel Resende Machado
  • Carla Chrystina de Castro Pacheco Ferreira
  • Edward Hermann Haeusler
  • Jefferson Barros dos Santos
  • Edmilson Varejão
  • Pedro Schneider
  • Athos dos Santos Barbosa
  • Maurício Magalhães
  • Marcelo de Carvalho Researcher
  • Ana Cristina de Freitas Marotti




Analytical Models, Predictive Maintenance, Intelligent Maintenance, Machine Learning, Agnostic PAC Learning, HV Substations


This work aims to use Data Science techniques to build predictive models that will eventually improve maintenance plans regarding power transformers by reducing shutoffs and transmission downtime. This work is part of a 36-month long Research and Development (R&D) project started on January 2021, as of the writing of this report the project is halfway through. The analytic models described herein have already been tested while most of the remaining work is yet to be done. In a single Data Lake environment, we will consolidate several databases from information systems that support the company's operation and maintenance processes. Various machine learning models will run on this data, and their results will appear in a dashboard, alongside several traditional indicators. This process will be integrated and consolidated in a computing platform with cloud architecture. We use Machine Learning (ML) to develop the models. Based on an Agnostic Probably Approximately Correct (PAC) Learning study of the available datasets estimating their Vapnik–Chervonenkis dimension, we choose Random Forests (RF) algorithms to be used in the new indicators. So far, the project has produced two new indicators: Chromatographic Assay Indicator (CAI) and Electrical Failure Risk Indicator (EFRI). The CAI indicator evaluation uses a Random Forest Algorithm trained with an external dataset due to the small number of power transformers failures in the O&M data. This indicator performed much better than classical chromatographic indicators to predict electric or temperature problems on the test set. The EFRI indicator correlates monitoring data available from an existing Supervisory Control and Data Acquisition (SCADA) system with maintenance data from an existing Enterprise Resource Planning (ERP) system through a RF algorithm capable of alerting to a higher risk of electric failure. ANEEL funds this work as R&D project PD-00394-1907/2019 titled “Aplicabilidade de nova tecnologia voltada para o desenvolvimento de um modelo de monitoramento inteligente dos ativos de transmissão”.