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The key to intelligent fault diagnosis for transformer is to find relevant dissolved gas characteristics with the capability of representing different types of faults. However, the existing problem is that using a single traditional ratio method as the inputs of classification module cannot obtain high classification performance. Therefore, a fault diagnosis method for transformer based on neighborhood component analysis (NCA) and capuchin algorithm (CapSA)-regularized extreme learning machine (RELM) is proposed. Firstly, 21-dimensional features of the oil-immersed transformer are extracted based on the traditional ratio methods such as IEC three-ratio and Roger's ratio. Secondly, The NCA is used to select the feature to obtain an effective 6-dimensional feature. Then, these features are utilized as the input of the RELM model for training, and the parameters of model are optimized by CapSA. Finally, the feasibility and validity of the proposed method are verified by actual data sets collected from previous publications. The experimental results show that the proposed method has higher diagnostic precision and stability. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unsatisfactory diagnosis performance of conventional fault diagnosis methods for transformer, a novel method based on maximally collapsing metric learning algorithm (MCML) and parameter optimization kernel extreme learning machine (KELM) is proposed in this study. First, a new set of dissolved gas analysis (DGA) features combination, which can reflect the transformer fault information, is used to form the input feature space. Then, the MCML is employed to reduce the feature space dimension to extract a set of optimal DGA features combination. Finally, the salp swarm algorithm (SSA) is utilized to optimize the parameters in KELM to establish an SSA‐KELM model, which is adopted to diagnose and identify transformer faults. The proposed method is applied to the International Electrotechnical Commission (IEC) TC 10 database, and the results show that the feature extraction effect of MCML is superior than that of linear discriminant analysis, neighborhood preserving embedding, and Laplacian eigenmaps. The optimal DGA feature set is more advantageous than the frequent‐used DGA data, IEC ratios, Rogers ratios, and Doernenburg Ratios. The diagnosis accuracy of SSA‐KELM is better than that of KELM, particle swarm optimization‐KELM, genetic algorithm‐KELM, and loin swarm optimization‐KELM. Furthermore, the generalization and robustness ability of the MCML and SSA‐KELM is confirmed by the China DGA samples, the obtained results verify the reliability and validity of the proposed method again. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Intelligent condition identification is the development trend of transformer condition recognition, However, the existing intelligent condition recognition has the disadvantage of single model and low recognition accuracy. In order to overcome the disadvantage, a transformer condition recognition method based on dissolved gas analysis features selection and multiple models fusion is proposed. At first, artificial bee colony (ABC) algorithm is employed to select a set of optimal dissolved gas analysis (DGA) features. Then, according to the selected feature set, support vector classifier (SVC) model, naive bayes classifier (NBC) model, and back-propagation neural network (BPNN) model are respectively established. Finally, the Dempster–Shafer​ (D-S) evidence theory is used to combine the output results of the three recognition models. The identification performances of the optimal DGA features set and the multiple models fusion are evaluated. The results reveal that the proposed method has the highest accuracy and consistency of 89.39% and 90.49% respectively. With the input vector of proposed optimal DGA features set, the overall performance of multiple model fusion method is superior than the SVC, NBC, BPNN, extreme learning machine (ELM) and K- Nearest-Neighbor (KNN). The accuracy and consistency are increased by 4.54%, 4.52%, and 10.42%, 14.68%, and 3.03%, 3.67%, and 10.6%, 13.45%, and 24.24%, 27.57% respectively. Also, the overall performance of multiple model fusion method with optimal DGA features set is increased significantly compared to multiple model fusion method with IEC Ratio, Rogers Ratio, Doernenburg Ratio, optimal DGA features set selected by particle swarm optimization (PSO) and harris hawks optimization (HHO). The accuracy and consistency are increased by 4.54%, 7.43%, and 6.06%, 7.33%, and 10.6%, 13.58%, and 1.51%, 1.39%, and 9.09%,10% respectively. This proves the effectiveness and the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]