Efficient and Effective

Transformer Care

Project Data

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New non-intrusive condition monitoring equipment which monitors the voltage and current of transformers are being installed. This equipment uses a self-learning algorithm to predict future faults.

What is the project about?

A new type of condition monitoring equipment has been created which involves using the measurements from Current Transformers (CT’s) and Voltage Transformers (VT’s). A self-learning algorithm is then used to establish the existence of any minute losses and their source. This technology can also be used to predict developing faults and identify a remedial solution, therefore extending the life of the asset.

How we’re doing it

This project will consist of the trial of the Engaging Online Transformers Monitoring Solution on eligible UKPN assets, in order to:

  • Establish if the new technology is a good alternative to existing technologies and can be implemented on UKPN assets as a BAU application.
  • Establish if the technology can identify issues before existing condition-monitoring equipment can.

The diagnostic system to be implemented is based exclusively on the acquisition of electrical quantities (voltages and currents of the transformers windings), being completely non-invasive and of easy practical installation.

What makes it innovative

With this diagnostic system, failures in transformer winding cores and faults in tap-changers can be detected. The processing module of the diagnostics system will be connected to an Ethernet network. The results will be accessible through the TransfoView software, which stores the information collected and processed by the diagnostic system.

What we’re learning

By installing the novel condition monitoring equipment, developing faults inside the transformer will become visible and thus preventable. As a result of the non-intrusive nature of this solution, it can be scaled with minimal business disruption. This will unlock more accurate business planning and maximising asset operation. Additionally, the project sets out to investigate if the new information collected can enable a more efficient transformer maintenance programme.

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