Abstract
Onset of incipient faults in power transformers can degrade the mineral oil and cellulose insulation, leading to the formation of dissolved gases. The process from oil sampling to quantification of gases is known as dissolved gas analysis (DGA). Despite the availability of DGA interpretation schemes and artificial intelligence (AI) methods for transformer condition monitoring (CM) based on DGA data, it is pointed out in this paper that these approaches are less than ideal and practical in implementation. In view of that, this paper illustrates a novel approach for analysis and interpretation of DGA data, which leads to a more credible CM of power transformers. The proposed approach, which is based on the self-organising map (SOM) algorithm, has been validated using real fault-cases and thereby is proven to be more reliable in portraying the current condition of power transformers
| Original language | English |
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| Pages | 1881-1886 vol.3 |
| Publication status | Published - 2001 |
| Event | Power Engineering Society Summer Meeting, 2001. IEEE - Duration: 1 Jan 2001 → … |
Conference
| Conference | Power Engineering Society Summer Meeting, 2001. IEEE |
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| Period | 1/01/01 → … |
Keywords
- mineral oil
- self-organising map algorithm
- dissolved gases formation
- insulation testing
- cellulose insulation
- power transformers incipient faults detection
- power engineering computing
- self-organising feature maps
- power transformer testing
- power transformer insulation
- artificial intelligence
- fault diagnosis
- condition monitoring
- transformer condition monitoring
- electric breakdown
- dissolved gas analysis
- computerised monitoring