Health Index Calculation for Distribution Transformer and Fault Identification
Keywords:
Health Index, Distribution Transformer, Weight Factor, Fuzzy LogicAbstract
Background and Objectives: Distribution transformers are critical components in electrical power systems. Therefore, regular annual maintenance is essential to prevent operational failures. In typical distribution networks, a large number of transformers are installed, each exhibiting different conditions depending on age and operating environment. Identifying the condition of each transformer enables utilities to plan appropriate maintenance activities and frequencies, thereby reducing costs and improving operational performance. This research aimed to propose a method that can be used to determine the health index of distribution transformers to support the development of condition-based maintenance plans.
Methodology: The health index of distribution transformers was assessed by employing the weighted scoring method and the fuzzy logic method. The factors used for the assessment included dissolved gases in oil, breakdown voltage strength of the insulating oil, moisture content in oil, and transformer service age. Both methods were applied to the condition monitoring data obtained from 23 distribution transformers. Furthermore, patterns of internal transformer faults were identified by using the IEC gas ratio method integrated with fuzzy logic.
Main Results: The analysis of transformer health indices obtained from both methods reveals that consistent condition levels were produced for 6 transformers, while the remaining 17 units showed discrepancies. Among these, 15 transformers were classified by the fuzzy logic method as having worse health levels by one grade level compared with the weighted scoring method; 2 transformers were classified as worse by two grade levels. The fuzzy logic approach was found to provide more appropriate health-level classifications as the weighted scoring method relies on fixed boundary ranges for each factor, whereas fuzzy logic enables more flexible boundaries suitable for multi-factor evaluation without requiring rigid criteria. In addition, the fault pattern analysis indicates that internal faults occurred in five of the distribution transformers.
Conclusions: Regular maintenance of distribution transformers is crucial. Implementing condition-based maintenance plan can help reduce outages and enhance system reliability. While the weighted-score method is simpler and easier to implement, it is less effective in differentiating conditions when input factors are ambiguous. In contrast, fuzzy logic more accurately reflects real transformer conditions. Integrating the IEC gas ratio method with fuzzy logic enhances the accuracy of fault identification, enabling more effective maintenance planning.
Practical Application: The transformer health index results can support prioritization of maintenance activities based on transformer criticality, particularly for distribution utilities or facilities with a large number of transformers. This approach helps reduce maintenance time and labor requirements and promotes more cost-effective resource allocation.
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