Fault Diagnosis Method of Flexible Converter Valve Equipment Based on Ensemble Empirical Mode Decomposition and Temporal Convolutional Networks

Abstract

The flexible converter valve is a crucial component of the flexible transmission system, and its proper functioning is directly related to the power system's reliability. This study proposes a method for diagnosing faults in flexible converter valve equipment based on ensemble empirical mode decomposition and temporal convolutional networks. The method involves measuring voltage signal data in the power submodule of the flexible converter valve, decomposing and reconstructing the voltage signal using ensemble empirical mode decomposition to extract frequency variation patterns and fault features. Subsequently, a temporal convolutional network is introduced, and a device fault diagnosis model is constructed by learning the evolution law of voltage signals in time series. The experimental results demonstrate that the proposed method has high fault diagnosis accuracy and robustness with an average F1-score of 89.58% and an average area under the curve (AUC) of 94.38%, which are higher than other methods by at least 1.39% and 1.03%.