Top oil temperature (TOT) is an important parameter to evaluate the running state of a transformer. According to the variation trend of TOT, the internal thermal state of transformers can be predicted so as to arrange operation and maintenance reasonably and prevent the occurrence of accidents. However, due to the complex working environment in the field, there are often a large number of missing values in online monitoring data, which seriously affects the prediction of TOT. At the same time, it is affected by various factors such as load, ambient temperature, wind speed, and solar radiation, which cause the information of different time scales to be mixed in its monitoring data. Therefore, it is difficult to achieve the desired accuracy with a single model. In this article, a model for predicting TOT based on data quality enhancement is proposed. First, the Markov model is used to complete the online monitoring data containing missing values to obtain a complete and continuous time series. Then, using the ensemble empirical modal decomposition method, the time series of TOT is decomposed into multiple time series components to eliminate the interaction between different time scales of information, thus reducing the prediction difficulty. Finally, the sub-prediction model of the extreme learning machine is constructed, and the prediction results of all the sub-models are reconstructed to obtain the final prediction results of TOT. In order to verify the effectiveness of the model, the TOT of an operating transformer for the next two days is predicted in the article, and its mean absolute percentage error (MAPE) is 5.27% and its root mean square error (RMSE) is 2.46. Compared with the BP neural network model and the support vector machines (SVM) model, the MAPE is reduced by 69.56% and 61.92%, respectively, and the RMSE is reduced by 67.02% and 59.87%. The results of this study will provide important support for the fault diagnosis of the top oil temperature online monitoring system.
To enhance the solubility of hydrophobic nutrients, the hydrophobicity of fish scale gelatin hydrolysate (FSGH) was increased with moderate acid or alkali hydrolysis. Acid-induced FSG hydrolysate (AcFSGH) at 3 h showed a superior curcumin loading efficiency (18.30 ± 0.38 μg/mL) among all FSGHs. Compared with FSG, the proportion of hydrophobic amino acids (from 41.1% to 46.4%) and the hydrophobic interaction (from 12.72 to 20.10 mg/mL) was significantly increased in the AcFSGH. Meanwhile, the transformation of the α-helix (from 12.8% to 4.9%) to the β-sheet (from 29.0% to 42.8%) was also observed in the AcFSGH. Based on the observation in the molecular weight and morphological analysis, AcFSGH acquired the best hydrophobic interaction with curcumin, presumably due to the formation of the flexible structure of the linear hydrolyzates. The above results call for an investigation of the role of FSG hydrolysate in the synthesis of nanoparticles loaded with bioactive lipophilic compounds.