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Insulation systems of electrical apparatus (fluid and solid) include transformer oil, insulating paper, glass, ceramic, mica and rubber. Normally, they are designed well to be stable at high temperatures and provide excellent electrical insulating properties in different operating loads. There is an increasing demand to modify the effectiveness and capabilities of these insulation systems to overcome various issues (e.g., overheating, overloading and short circuits). Moreover, they should match the new high voltage insulation requirements, safety, ultra-high voltage, and being environmentally friendly are some of them. Nanoparticles (NPs) are promising compensator materials that can be used to develop insulation systems with good thermophysical and dielectric properties. The results of many studies show marvelous enhancements in electrical and thermal properties. In this paper, the authors present a review that focuses on different NPs that are used in several aspects of insulation systems to improve the dielectric as well as the heat transfer properties of high voltage insulation applications. The review covers NPs types, the preparation methods, analysis approaches, different properties, performances and results, challenges, and the limitation of the use of NPs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively. [ABSTRACT FROM AUTHOR]