Converting data into knowledge for preventing failures in power transformers

Ricardo Manuel Arias Velásquez, Jennifer Vanessa Mejía Lara, Andres Melgar

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

This research is performed in order to estimate the overall condition of power Transformers, it is considered for 60 power transformers and 10,198 electrical test: Factory and site acceptance test, quality oil test, insulation test, among others. The Health Index (HI) is useful for maintenance strategy planning. The aim of this research is to represent the new method to assess the conditions of transformer by applying a new developed HI method; it has been improved from the traditional method by using orthogonal Wavelet network for long-term degradation factors, that cumulatively lead to the transformer span life and validate by the data mining process. It has been proved with machine learning algorithms with high accuracy. Rich metadata is required to find and understand the measurements, from modern experiments with their immense and complex data stores, the new method allows to structure a more practical way to categorization with various components, for a better decision science with a multi planning strategy. To store and manage these metadata have improved over time, but, mostly of the cases are ad-hoc collections of data relationships, often, represented in domain or site specific application code. A case study with 60 power transformer is made to evaluate the performance of the new develop HI. Therefore, the proposed method can create an efficient preventive maintenance plans for power transformers; it is an important contribution for knowledge, because the data is converting into knowledge.

Original languageEnglish
Pages (from-to)215-229
Number of pages15
JournalEngineering Failure Analysis
Volume101
DOIs
StatePublished - Jul 2019

Keywords

  • Health
  • Knowledge
  • Machine learning
  • Transformers

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