Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults

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

Research on the error probability distribution of photovoltaic output prediction based on output fluctuation characteristics and Generalized Gaussian Mixture Model

Photovoltaic power output forecast error exists objectively and inevitably, and it can provide a guarantee for safe and stable operation of the power system through analyzing its characteristics. In this paper, the influence of predicted output fluctuation characteristics (predicted output amplitude and power variation) on prediction error was studied based on the analysis of variance (ANOVA) method. The prediction error conditions were classified into six types based on the clustering of numerical characteristics of predicted output. Then, a Generalized Gaussian Mixture Model (GGMM) was proposed to fit the prediction error distribution of each type of photovoltaic output. The mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE) were used as accuracy evaluation indexes. The example analysis showed that the GGMM can satisfy the asymmetry and kurtosis diversity of the error distribution after division by conditions, and the fitting result is better than that of the normal distribution, improved Laplace distribution and t Location-Scale distribution model

Enhanced Electrical Insulation and Heat Transfer Performance of Vegetable Oil Based Nanofluids

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 nano-oil 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

Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults

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

Gas diffusion behavior in green camellia insulating oils

As a new environmentally friendly liquid dielectric material, vegetable insulating oil has been widely used in oil-filled power equipment. In oil-filled power equipment, ageing, faults of overheating and discharge cause the decomposition of insulating oil and insulating paper, resulting in dissolved gases in oils. The diffusion behavior of dissolved gases in oils is helpful for evaluation of health state of oil-filled power equipment. In this study, the molecular dynamics simulation based on polymer consistent force field (PCFF) is adopted to analyze diffusion processes of dissolved gases in camellia insulating oils. The diffusion coefficients and free volume of dissolved gases including hydrocarbons, carbon oxides and hydrogen are calculated. The diffusion trajectory of dissolved gases in oils are also given. In addition, impacts of gas species and temperature on molecular diffusion coefficients of oils were also studied. Results quantitatively describe the diffusion behavior of gases with different molecular weight in the oils under various temperatures. The research provides theoretic support for further application of vegetable insulating oils in power equipment

Efficient heterogeneous Fenton-like degradation of methylene blue using green synthesized yeast supported iron nanoparticles

To reduce the consumption of oxidant and catalyst in Fenton-like reaction and to realize the reuse of catalyst, yeast supported iron nanoparticles (nZVI@SCM) was synthesized by tobacco leaf extract and applied in the heterogeneous Fenton-like degradation of aqueous methylene blue (MB) at ambient conditions. The performance of the composite was exploited in terms of catalytic activity and factors influencing MB degradation. The surface changes of nZVI@SCM before and after reaction were characterized by XPS, SEM, FT-IR and XRD. Iron leaching, primary reactive oxidizing species, and the storage stability and reusability of catalyst were also investigated. Typically, 99.7% removal of 50 mg/L MB, with a TOC removal of 97.2%, could be achieved within 10 h by 0.1 g/L nZVI@SCM coupled with 1.0 mM H2O2. The MB degradation is in good agreement with the pseudo-first-order model, and hydroxyl radicals in the bulk solution is the main reactive oxidizing species responsible for MB degradation. Based on the identified intermediates by liquid chromatography/mass spectrometry, the possible MB degradation mechanism in the nZVI@SCM/H2O2 system is discussed. The developed high-performance nZVI@SCM catalyst strategy can provide a new route in enhancing the Fenton-like degradation of organic contaminants with less consumption of catalyst and oxidant

Early Warning of High-Voltage Reactor Defects Based on Acoustic–Electric Correlation

Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic–electric fusion high-voltage reactor acquisition system and defect diagnosis method based on reactor pulse current and ultrasonic detection signal. Using the envelope peak signal as the basic detection data, the sampling requirement of the system is reduced. To fill the missing data with partial discharge (PD) information, a method based on k-nearest neighbor (KNN) is proposed. An adaptive noise reduction method is carried out, and a noise threshold calculation method is given for the field sensors. A joint analysis method of acoustic and electrical signals based on correlation significance is established to determine whether a discharge event has occurred based on correlation significance. Finally, the method is applied to a UHV reactor on the spot, which proves the effectiveness of the method proposed in this paper