基于Bagging算法的水电机组状态评估
Hydropower Unit Condition Assessment Based on Bagging Algorithm
林峰平 1吴子豪2
作者信息
- 1. 深圳市康必达控制技术有限公司 深圳 518000
- 2. 湖北工业大学 电气与电子工程学院 湖北 430068
- 折叠
摘要
水电机组作为重要的发电设备,其健康状态的评估对于保障电力供应的稳定性和安全性至关重要.传统的状态评估方法多依赖于单一的参数或经验判断,存在一定的局限性.本文提出一种创新的方法,通过集成学习中的Bagging算法,构建一个水电机组健康状态模型,通过对劣化度的大小判断,以实现对水电机组状态的准确评估.研究中,首先收集了水电机组在不同工况下的多种参数,包括温度、电流、电压、有功功率以及上导X向摆度等数据.这些数据被输入到Bagging算法中,以建立工况参数与上导X向摆度之间的相关性.接着,将实时的工况参数输入到训练好的模型中,模型输出的理论值与实际的上导X向摆度进行比较,通过差值比分析,得到机组的实时劣化度.最后,通过劣化度评估得到机组的实时状态.这一过程不仅能揭示机组状态变化的规律,而且能为机组的维护和检修提供科学依据.
Abstract
As crucial power generation equipment,assessing the health status of hydropower units is vital for ensuring the stability and safety of power supply.Traditional condition assessment methods often rely on single parameters or expert judgment,which have certain limitations.This study proposes an innovative method by constructing a health status model for hydropower units using the Bagging algorithm in ensemble learning.The assessment is achieved by determining the degree of degradation,thereby enabling accurate evaluation of the unit's condition.In the study,various parameters of hydropower units under different operating conditions were collected,including temperature,current,voltage,active power,and upper guide X-direction vibration data.These data were input into the Bagging algorithm to establish the correlation between operating parameters and the upper guide X-direction vibration.Then,real-time operating parameters were input into the trained model,and the theoretical values output by the model were compared with the actual upper guide X-direction vibration.Through difference ratio analysis,the real-time degradation degree of the unit was obtained,and the unit's real-time condition was ultimately assessed based on the degradation degree.This process not only reveals the pattern of condition changes in the unit but also provides a scientific basis for the maintenance and repair of the unit.
关键词
状态评估/Bagging算法/劣化度评估Key words
Condition Assessment/Bagging Algorithm/Degradation Degree Evaluation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据