Caldas Hydroelectric Power Plant - CHEC

Application of algorithms for analysis and monitoring of electrical variables and power quality to identify behaviors that indicate a risk of failure in substations in the industrial sector of Manizales.

Detected needs:

Leverage existing information for the generation of early warnings associated with the occurrence of faults in network circuits.

Preventive maintenance to improve service quality.

This project made it possible to take advantage of and exploit the data on electrical variables related to power quality, which are collected by Information Acquisition Units (UAD's).

The results of the project were deployed in a web platform that integrates ARCGIS services and can be accessed from the company's intranet.

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Thanks to this project it was possible to

- Establish a diagnosis and initial recommendation on data access and structuring, allowing a better preparation to address data science solutions in the organization.

- The results of a methodology/model that can be scalable to the DAUs that monitor various circuits in the city were validated.

- The importance of implementing data science and machine learning projects to optimize their business processes was shown, and they are now looking to increase investment in this type of solution.

Technologies implemented

  • Data Science

  • HPC

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