Dissolved gas analysis (DGA) is attracting greater and greater interest from researchers as a fault diagnostic tool for power transformers filled with vegetable insulating oils. This paper presents experimental results of dissolved gases in insulating oils under typical electrical and thermal faults in transformers. The tests covered three types of insulating oils, including two types of vegetable oil, which are camellia insulating oil, Envirotemp FR3, and a type of mineral insulating oil, to simulate thermal faults in oils from 90˝C to 800˝C and electrical faults including breakdown and partial discharges in oils. The experimental results reveal that the content and proportion of dissolved gases in different types of insulating oils under the same fault condition are different, especially under thermal faults due to the obvious differences of their chemical compositions. Four different classic diagnosis methods were applied: ratio method, graphic method, and Duval's triangle and Duval's pentagon method. These confirmed that the diagnosis methods developed for mineral oil were not fully appropriate for diagnosis of electrical and thermal faults in vegetable insulating oils and needs some modification. Therefore, some modification aiming at different types of vegetable oils based on Duval Triangle 3 were proposed in this paper and obtained a good diagnostic result. Furthermore, gas formation mechanisms of different types of vegetable insulating oils under thermal stress are interpreted by means of unimolecular pyrolysis simulation and reaction enthalpies calculation.
The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT) faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA) systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS) is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.
Nanoparticles enhance the electrical insulation and thermal properties of vegetable oil, and such improvements are desirable for its application as an alternative to traditional insulating oil for power transformers. However, the traditional method of insulating nanofluids typically achieves high electrical insulation but low thermal conductivity. This work reports an environmentally friendly vegetable oil using exfoliated hexagonal boron nitride (h-BN) showing high thermal conductivity and high electrical insulation. Stable nanofluids were prepared by liquid exfoliation of h-BN in isopropyl alcohol. With 0.1 vol.% of the nano-oil, the AC breakdown voltage increased by 18% at 25 ∘ C and 15% at 90 ∘ C. Both the positive and negative lightning impulse breakdown voltages of the nanooil were also enhanced compared with those of the pure oil. Moreover, the thermal conductivity of the nano-oil increased by 11.9% at 25 ∘ C and 14% at 90 ∘ C. Given its high thermal conductivity, the nano-oil exhibited faster heating and cooling effects than the pure oil. Nano-oils with an electric field (either DC or AC) displayed a faster thermal response than that without an electric field. The reason is that h-BN is oriented under the electric field and formed a thermal network to increase the heat transfer.
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