A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

Abstract

The utilization of data-driven artificial intelligence technology for ultra-high voltage converter valve state assessment can improve efficiency and accuracy, but there are still problems such as imbalance of raw data and poor model interpretability. This paper aims to propose a new type of ultra-high voltage converter valve state assessment method to solve these problems. An oversampling method that considers characteristic boundary information is proposed, which can retain and enhance the boundary information of minority samples. The balanced data are imported into categorical boosting to realize offline training and online evaluation of converter valve state assessment. An interpretability analysis framework of the converter valve state assessment model based on the Shapley additive explanations is proposed, which can explain the operation logic of the model and find the key factors that affect the assessment results. The calculation example shows that the evaluation accuracy of this method can reach up to 98.5%, and the running speed is faster than the conventional method. The results can provide guidance for the maintenance and repair of UHV converter valves.