Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

Zhang, Mei; Chen, Wanli; Zhang, Yu; Liu, Fei; Yu, Dongshun; Zhang, Chaoyin; Gao, Li

doi:10.1109/access.2021.3135283

Cited by 17 publications

(9 citation statements)

References 41 publications

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“…In order to make the model more convincing, the GA-XGBoost diagnostic model proposed in Ref. 36 . and The PSO-BiLSTM diagnostic model proposed in Ref.…”

Section: Resultsmentioning

confidence: 99%

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

Wang,

Chi,

Ding

et al. 2024

Sci Rep

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In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.

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“…In order to make the model more convincing, the GA-XGBoost diagnostic model proposed in Ref. 36 . and The PSO-BiLSTM diagnostic model proposed in Ref.…”

Section: Resultsmentioning

confidence: 99%

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

Wang,

Chi,

Ding

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For the iterations, too small a parameter can lead to underfitting, resulting in inadequate model resolution, while too large a parameter can lead to overfitting, resulting in a decrease in the generalization ability of the model. In addition, the choice of depth is also important, as a wrong choice can affect the learning ability and classification capability of the model [ 41 ]. Therefore, in this study, an optimization algorithm was chosen to optimize the above four parameters to improve the performance of the classification model.…”

Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

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Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.

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“…XGBoost was chosen because of its new regularization method that resists overfitting, therefore making the model more robust [31,50]. CatBoosts improved generalization as well as its ability to capture high-order dependencies makes it a viable candidate as well [51]. Last but not least LightGBM is an attractive method to use as a classifier because of its capability in handling large-scale data.…”

Section: Discussionmentioning

confidence: 99%