TITLE:
Development and Data-Driven Internal Consistency of a Multi-Parameter Health Index for Power Transformer Condition Assessment
AUTHORS:
Kone Gbah, Gbegbe Raymond
KEYWORDS:
Power Transformers, Health Index, Condition Assessment, Principal Component Analysis, Asset Management
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.9,
September
7,
2026
ABSTRACT: Reliable transformer condition assessment requires the combined interpretation of several diagnostic measurements, as no single test can fully describe the condition of the oil-paper insulation system. This study develops and validates a multi-parameter Health Index (HI) for power transformers using field data collected from 101 in-service units operating under different voltage levels, powers ratings and ages. The proposed method combines dissolved gas, moisture, dielectric performance, acidity, and cellulose-aging indicators to calculate a HI score ranging from 0 to 100. Based on this score, the transformers were divided into five condition groups, from very poor to very good. The results obtained show that 35 units are in very good condition, while 11 transformers fall within the most degraded range and require particular attention. In addition, the principal component analysis (PCA) was used to examine whether the different HI groups were consistent with the diagnostic patterns. The first two principal components explained 63.58% of the total variance and revealed a gradual transition from healthy units, characterized by high breakdown voltage and limited degradation, to the deteriorated transformers with increased moisture, dissolved gases, acidity, and aging products. K-means clustering revealed partial agreement with the HI groups, particularly for the extreme condition, while showing greater overlap among the intermediate categories. A case study involving two transformers also demonstrated that similar HI ranges may result from distinct degradation mechanisms. Consequently, it is necessary to interpret the HI score together with individual test results. The proposed approach provides a practical tool for utilities to assess transformer fleets, compare equipment condition, prioritize maintenance activities, and support risk-informed asset management decisions.