Parametric noise reduction and construction of performance degradation indexes for low-voltage switchgear appliances

Aiming at the problem that there is a lot of noise in the feature parameter data of low-voltage switchgear, which makes it difficult to extract useful information about the feature parameters and unable to construct the performance degradation index reasonably. This paper proposes a noise reduction method through Relative Entropy (KL) optimized Variational Mode Decomposition (VMD) combined with Non-Local Mean (NLM) and a method of constructing performance degradation indexes for low-voltage switching appliances through Convolutional Autoencoder (CAE). Firstly, the voltage and current signals are collected based on the full-life test platform of low-voltage switchgear, and the subset of feature parameters is extracted. Then, the feature parameters are decomposed into different components by relative entropy optimization VMD and the noise-dominant and effective components are selected based on the correlation. Secondly, NLM denoising is performed on the noise-dominated signal, and the noise-reduced signals are obtained by signal reconstruction. Finally, the construction of performance degradation indexes for multi-type switchgear is completed based on CAE. The final results show that the two noise reduction evaluation indicators including Noise Rejection Ratio (NRR) and trendiness (T), and the three performance degradation evaluation indexes including Goodness of Fit (R), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) all show optimal results, indicating that this paper’s method can better complete the construction of noise reduction and performance degradation indicators for the feature parameters of low-voltage switchgear.