基于改进极限学习机的水轮机运转状态识别

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (1) : 132-138.

论文

基于改进极限学习机的水轮机运转状态识别

兰朝凤，宋博文，郭小霞

作者信息 +

Identification of hydraulic turbine operation state based on improved limit learning machine

LAN Chaofeng, SONG Bowen, GUO Xiaoxia

Author information +

文章历史 +

摘要

针对水轮机远程运行状态监测困难的现状，本文提出了基于模拟退火算法的粒子群算法（Simulated annealing algorithm-Particle swarm optimization, SA-PSO）优化极限学习机（Extreme learning machine, ELM）的方式，建立水轮机运转状态识别模型SA-PSO-ELM。对水轮机信号进行互补集合经验模态分解（Complementary ensemble empirical mode decomposition, CEEMD）获得IMF分量，引入皮尔逊相关系数计算各个IMF分量与水轮机信号的相似度，判断信号主导模态和噪声主导模态的分界点，用小波去噪对噪声主导模态降噪，并与其余的IMF分量重构得到去噪信号，同时对去噪后的信号进行分解，计算每个IMF分量的排列熵，构建特征向量。由于SA-PSO精度高不易陷入局部最优的特点和ELM的性能受权值、阈值共同影响的特点，用SA-PSO优化ELM的权值和阈值，构建水轮机运转状态识别模型SA-PSO-ELM。对不同工况下采集的水轮机压力脉动数据，分析了基于PSO-ELM、ELM 及SA-PSO-ELM模型的水轮机运转状态识别正确率、均方误差、决定系数3个指标。结果表明：SA-PSO-ELM更适合于水轮机运转状态识别。
关键词：水轮机；互补集合经验模态分解；粒子群算法；极限学习机；状态识别

Abstract

导出引用

兰朝凤，宋博文，郭小霞. 基于改进极限学习机的水轮机运转状态识别[J]. 振动与冲击, 2023, 42(1): 132-138

LAN Chaofeng, SONG Bowen, GUO Xiaoxia. Identification of hydraulic turbine operation state based on improved limit learning machine[J]. Journal of Vibration and Shock, 2023, 42(1): 132-138

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