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

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

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PDF(2116 KB)
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

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

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

Key words

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

引用本文

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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