In order to reduce the downtime and maintenance time of a DC system and improve the stability of converter valve operation

a state evaluation method for UHV converter valve based on multi-state data equalization and extreme gradient boost（XGBoost） is proposed. Firstly

for the main components of the thyristor converter valve

the four characteristic indexes of the thyristor component

the valve cooling component

the valve arrester and the external environment are extracted. A data preprocessing method based on isolated forest and synthetic minority oversampling technology is proposed

where the outlier samples in the data set are removed

and then the minority samples are oversampled to achieve the effectiveness and balance of the data set in each state. The preprocessed data are utilized to train the XGBoost classifier

and the optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search method. Finally

the measured data of a converter station in Jiangsu Province are taken as an example to verify the proposed method

and the results show that the accuracy rate of the evaluation model is 97.1%

which is more accurate than traditional methods for judging the operating state of the converter valve. In addition

the model can reflect the characteristic contribution of each state quantity

and provide a basis for the maintenance of the converter valve.