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On-load tap changers (OLTC) have complex mechanical and electrical structures, which are the key component for the on-load voltage regulation of transformers. Currently, due to the sample data which are not easy to be labeled, it is difficult to effectively train the OLTC mechanical fault diagnosis models based on vibration signals. To improve the fault diagnostic accuracy for OLTC with limited labeled data, a fault diagnosis method based on Bayesian optimization-convolutional ladder networks (BO-ConvLN) is proposed in this paper. Firstly, the ladder networks are used as a semi-supervised learning method for the feature extraction of vibration signals, which is guided by a large amount of unlabeled data, leading to the enhanced diagnostic ability of ladder networks only with a small amount of labeled data. Then, the fully-connected layers are replaced by convolutional operators in the ladder networks to better extract the features of non-stationary vibration signals. Furthermore, Bayesian optimization is used to optimize the high-dimensional hyperparameters of ladder networks, witch significantly improves the diagnostic accuracy of the model within limited time cost. The experiment results show that the diagnostic accuracy for the three types of faults, namely transmission shaft jams, poor switch lubrication, and top cover looseness, is 91.67% with a label count of only 40, which demonstrates the effectiveness of BO-ConvLN in the fault diagnosis.

In this study, a phase-generated carrier (PGC) demodulation algorithm combined with the extended Kalman filter (EKF) algorithm based on an electro-optic modulator (EOM) is proposed, which can achieve nonlinear distortion (such as modulation depth drift and carrier phase delay) suppression for high-frequency phase carrier modulation. The improved algorithm is implemented on a field-programmable gate array (FPGA) hardware platform. The experimental results by the PGC-EKF method show that total harmonic distortion (THD) decreases from −32.61 to −54.51 dB, and SINAD increases from 32.59 to 47.86 dB, compared to the traditional PGC-Arctan method. This indicates that the PGC-EKF demodulation algorithm proposed in this paper can be widely used in many important fields such as hydrophone, transformer, and ultrasound signal detection. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc. [ABSTRACT FROM AUTHOR]

Objective: To investigate the repairing effect of Lactobacillus acidophilus LA11-Only on ceftriaxone induced intestinal micronbiota imbalance in mice and its alleviating effect on lipid metabolism., Methods: A total of 30 SPF BALB/c male mice were randomly divided into control group(10) and model group(20). The model group was given ceftriaxone sodium solution by gavage for a week to create a mouse model of antibiotic flora disorder. The natural recovery group(10) was given 10% skimmed milk solution by gavage, and the LA11-Onlly group was given Lactobacillus acidophilus skimmed milk solution(1.0×10~9CFU/mL). Two weeks later, the feces were collected aseptically.16S rRNA sequencing technology and ultra-high-performance liquid chromatography combined with mass spectrometry(UPLC-MS) were used to analyze the repair effect of LA11-Onlly on antibiotic induced bacterial flora disorder and lipid metabolism in mice., Results: After ceftriaxone treatment, the diversity of intestinal microbiota in mice decreased, and there was a significant difference in the composition of flora compared with the control group; After treatment with LA11-Onlly, the Alpha diversity increased, the abundance of beneficial bacteria such as Lactobacillus and Butyricicoccus increased, and the composition of flora was closer to the control group; At the same time, the concentration of short chain fatty acids(SCFAs) increased in varying degrees. Compared with the natural recovery group, the levels of butyric acid and valeric acid in LA11-Onlly group increased significantly, close to the same level as the control group., Conclusion: Lactobacillus acidophilus LA11-Onlly probiotics can inhibit the growth of harmful bacteria by regulating the diversity and community composition of intestinal microbiota, promote the increase of the concentration of SCFAs, and alleviate the damage of antibiotics to the body.