Abstract: This paper presents a vibration prediction method of pumped storage units based on adaptive features and an optimized kernel extreme learning machine (KELM) to reduce the impact of the nonlinear, non-stationary characteristics of vibration signals on the accuracy of vibration predictions. First, we use improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to decompose a vibration signal and generate the intrinsic mode components of different frequencies. And, an autoencoder is used to extract the features of these components adaptively and capture their key features dynamically. Then, a KELM prediction model is developed to predict each component separately, using a modified DEIHHO algorithm to optimize its regularization parameter and kernel parameter. Finally, the final prediction result of unit vibration is obtained by superadding the predictions of all the components. Comparison with previous experimental data shows our new method is better in vibration prediction of pumped storage units and improves the accuracy effectively.

Key words: vibration prediction, adaptive feature, kernel extreme learning machine, improved complete ensemble empirical mode decomposition with adaptive noise, autoencoder, differential evolution-improved Harris hawk optimization