Transformer health index (THI) is a numerical tool for ranking transformers in a fleet according to condition and the need for attention. It brings selected condition-monitoring evidence into one score. Reliability, failure risk and remaining life are related questions, but they are not interchangeable with that score. Using THI for fleet management is like having an alarm bulb on your control panel: it directs attention, while the investigation still has to explain the warning.
How to obtain a transformer health index?
Typically, a fleet contains many transformers. Anyone managing these fleets is bound to collect a lot of information. However, identifying and removing redundancy from this information to decide which transformer needs immediate attention is very difficult. Therefore, transformers need to be ranked according to their intervention priority.
CIGRE Technical Brochure 761 describes transformer assessment indices for different purposes, including health or reliability, replacement, repair, refurbishment, composite assessment and mitigation. The intended decision determines which index and scoring method are appropriate; these labels are not a universal certification scheme.
A transformer health index can draw on condition-monitoring data, visual inspection, and operational and historical records. It helps answer two practical questions: which assets need attention first, and what evidence should we investigate next? An intervention timescale must come from the assessment method and engineering review, rather than from an unexplained score.
Calculation methods include weighted scores and scoring matrices. The choice must reflect the failure modes being assessed and explain how individual indicators affect the final ranking. The figure below summarizes approaches used for transformer assessment; it should not be read as a single mandatory formula.
How THI helps in fleet management?
A health index helps rank transformers for further review, including more frequent sampling or additional tests. It does not automatically provide a calibrated probability of failure. CIGRE TB 761, section 2.2, cautions that numerical scores commonly provide a ranking rather than a direct measure of reliability or remaining life. Combined with asset criticality and the consequences of an outage, that ranking can support decisions about where to spend the testing and maintenance budget.
What are the risks and limitations of using THI?
A single health score compresses information. Two transformers with similar scores may have different failure mechanisms and require different actions. The score alone cannot pinpoint a fault location or establish that replacement is necessary. Asset managers should follow screening with appropriate diagnostic investigation and engineering review before making major repair, refurbishment or replacement decisions.
Latest developments and the future of THI
AI and machine learning can assist with organizing and analyzing fleet data, provided their validation and limitations are clear. In the RONIN version discussed in this historical article, we used these techniques to support transformer health indexing. RONIN offers a way to review uploaded condition data across a fleet. Processing speed, supported batch sizes and predictive performance depend on the version and evaluation conditions; the interface does not remove the need to check inputs and investigate flagged assets.
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