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Dissolved gas analysis (DGA) method is widely used to detect the incipient fault of power transformers. This paper presents a novel DGA method for power transformer fault diagnosis based on Harris-Hawks-optimization (HHO) algorithm optimized kernel extreme learning machine (KELM). The non-code ratios of the gases are used as the characterizing vector for the KELM model, and the Harris-Hawks-optimization (HHO) algorithm is introduced to optimize the KELM parameters, which promotes the fault diagnostic performance of KELM. Based on dataset collected from IEC TC 10, the fault diagnosis capability of the proposed method is validated by different characterizing vectors and is compared with conventional KELM and other optimized KELM. Moreover, the generalization ability of the proposed method is confirmed by China DGA data. The results demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy.

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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.