Abstract:
Aiming to address the significant deviations in transformer fault identification when uneven fault data are processed, a hybrid integration model based on the improved li...Show MoreMetadata
Abstract:
Aiming to address the significant deviations in transformer fault identification when uneven fault data are processed, a hybrid integration model based on the improved light gradient boosting machine (GCLightGBM) is proposed in this study for effective transformer fault identification. This model consists of two key components. On the one hand, an improved LightGBM model called GCLightGBM is introduced. On the other hand, a hybrid integration model based on GCLightGBM is developed to resolve the problem that the recognition ability of the model is affected by parameter specificity values in GCLightGBM. GCLightGBM combines a gradient harmonized loss function (GHM Loss) with a cross-entropy loss function, which makes the model better focus on a few critical samples in the dataset. By doing so, GCLightGBM improves the performance of the model in identifying faults accurately. The hybrid integration model aims to further improve accuracy while ensuring that the model maintains a high performance on realistic variable unbalanced datasets. This approach improves the accuracy of fault identification while still rendering the dataset imbalanced. The experimental results demonstrate that GCLightGBM is effective in solving the low accuracy for certain classes of samples. In addition, the validity of the fault identification method based on the GCLightGBM hybrid integral model is validated on a publicly available dataset, making the model suitable for transformer fault identification.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 73)