EWP

Traditionally, transformer condition assessment has been based on periodic testing, typically defined according to the well-known “bathtub curve” which determines inspection frequency over the asset lifecycle.

Over the past decades, the increasing adoption of online monitoring systems has significantly changed this approach.

Condition assessment should no longer be based solely on scheduled tests, but on the actual operating condition of the transformer.

This evolution has led to what is commonly defined as condition-based or aging-oriented maintenance, enabling more accurate and timely decision-making.

Modern transformer assessment combines data from multiple sources:

The integration of these data allows a comprehensive and dynamic evaluation of transformer condition.

To simplify complex data into actionable information, EWP has developed its own Transformer Health Index (HI).

The Health Index is a synthetic numerical indicator that reflects the overall condition of the transformer, enabling:

Our model is based on hundreds of thousands of historical data sets with extensive field experience in transformer diagnostics and advanced data processing and engineering expertise.

EWP’s algorithms are fully integrated into the RM EYE platform, providing a centralized and intuitive overview of the entire transformer fleet, to enable:

By combining advanced monitoring systems with proprietary analytics, we help our clients move from reactive to predictive asset management. This results in improved reliability, optimized maintenance planning, and reduced risk of unexpected failures, while maximizing the performance and lifetime of transformer assets.