水力发电学报2025，Vol.44Issue(8)：20-30,11.DOI:10.11660/slfdxb.20250803
融合自适应特征与优化KELM的抽蓄机组振动预测
Vibration predictions of pumped storage units based on adaptive feature and optimized KELM
摘要
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.关键词
振动预测/自适应特征/核极限学习机/改进的自适应噪声完全集成经验模态分解/自编码器/差分进化-改进哈里斯鹰算法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分类
建筑与水利引用本文复制引用
付文龙,祝鑫锋,熊浩伟,相莹,邵孟欣,孔泽昊,孙政..融合自适应特征与优化KELM的抽蓄机组振动预测[J].水力发电学报,2025,44(8):20-30,11.基金项目
国家自然科学基项目(51741907) （51741907）
