Abstract
In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.
Highlights
Transformers asset management is one of the crucial aspects for utilities
The percentage of difference between health index (HI) obtained by scoring and NF-Monte Carlo Simulation (MCS) methods is much lower than NF-IS, with values of 8.2% and 56.3%, respectively
Based on the case study, it is found that the percentage of difference between HI of transformers obtained by scoring and NF-IS methods can be as high as 56.3%, while for NF-MCS, it is
Summary
Transformers asset management is one of the crucial aspects for utilities. With the current economic challenges nowadays, there is an urge to carry out comprehensive condition assessment of transformers in order to avoid early stage failures, which could be costly. Moisture and acidity are commonly monitored in the condition assessment scheme of transformers since these parameters are among the main ageing accelerators and by-products for oil and paper in transformers [6]. The conventional computation of HI is known as the scoring method where it is based on the ranking and scoring approaches where each condition assessment parameter is given a distinctive weighting factor These factors are normally determined based on either expert judgment or utility requirement [19]. For the ANN study, additional input parameters, such as loss angle and total solids, were included in the network topology where the in-service condition assessment data from 59 transformers were used for training and 29 transformers for testing. The HI of transformers that are obtained from the NF method are compared with the scoring method and analysed
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