Application of machine learning in state analysis of power plant equipment

LI Xiaodong; CHEN Yapeng; WANG Baoying; HU Qiaoyan; LAI Fei; WU Tao; XU Chuangxue; XUE Hanguang; HE Xin; WANG Zhiwei; GAO Haidong; GAO Lin

doi:10.19666/j.rlfd.201906193

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Application of machine learning in state analysis of power plant equipment

更新时间：2025-09-28

- Application of machine learning in state analysis of power plant equipment
- Thermal Power Generation Vol. 49, Issue 1, Pages: 129-133(2020)
- 作者机构：
  
  1. 中电投珠海横琴热电有限公司
  2. 西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201906193
  CLC： TM73
- Published Online：31 December 2019，
  
  Published：2020
- 稿件说明：
移动端阅览
LI Xiaodong, CHEN Yapeng, WANG Baoying, et al. Application of machine learning in state analysis of power plant equipment[J]. Thermal power generation, 2020, 49(1): 129-133.
DOI：

LI Xiaodong, CHEN Yapeng, WANG Baoying, et al. Application of machine learning in state analysis of power plant equipment[J]. Thermal power generation, 2020, 49(1): 129-133. DOI： 10.19666/j.rlfd.201906193.

摘要

本文对电站设备状态分析中学习向量量化(LVQ)神经网络和深度学习算法循环递归长短期记忆(LSTM)神经网络进行了详细分析

利用LSTM神经网络对磨煤机设备进行状态分析

将LSTM神经网络中最后一个隐含层的激励函数设为Softmax函数

其输出值表示设备状态的健康程度及设备可能发生事故的概率

并将LSTM神经网络和LVQ神经网络进行设备状态分析对比。结果表明

利用LSTM神经网络得到的训练模型可以得到设备状态分类更高的准确率

减少在设备状态评判中的漏报率和误报率。

Abstract

The learning vector quantitative(LVQ) neural network and deep learning algorithm cyclic recurrent longterm and short-term memory(LSTM) neural network in state analysis of power station equipment are analyzed in detail. The LSTM neural network is applied to carry out state analysis for the coal mill equipment

the excitation function of the last hidden layer in the LSTM neural network is set as Softmax function

and the output value of the LSTM neural network indicates the health degree of the device state and the probability that the device may have an accident. Moreover

the equipment state analysis result obtained by the LSTM neural network is compared with that of the LVQ neural network. The results show that

the training model obtained by the LSTM neutral network can obtain a higher classification accuracy and reduce the rate of missed and false alarm in equipment condition evaluation.

关键词

Keywords

references

Views

451

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Load optimal distribution of combined heat and power in gas turbine power plant using deep learning and chaotic optimization method

Advancements in the application of machine learning in circulating fluidized bed boiler technology

Prediction of key indicators of utility boiler based on multi-task uncertainty loss

Dynamic prediction of pollutants emission from circulating fluidized bed unit based on CNN-GRU-MHA

Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network

Related Author

LIU Gang

JIN Yiqun

CAO Xu

LAI Fei

CHAI Shengkai

WU Tao

HE Xin

WANG Zhiwei

Related Institution

State Power Investment Sihui Thermal Power Co., Ltd

Department of Energy and Power Engineering, Tsinghua University

Beijing Huairou Laboratory

Huairou Laboratory Shanxi Research Institute

School of Mechanical and Storage Engineering, China University of Petroleum

AI问答

⁰