山东电力技术2024，Vol.51Issue(5)：30-37,8.DOI:10.20097/j.cnki.issn1007-9904.2024.05.004
基于改进PSO-LSTM算法的风电机组状态监测方法研究
Research on Wind Turbine Status Monitoring Methods Based on Improved PSO-LSTM Algorithm
王印松 1刘佳微 1贾思宇 1翁疆2
作者信息
- 1. 华北电力大学控制与计算机工程学院,河北 保定 071003
- 2. 中国华电集团有限公司福建分公司,福建 福州 350002
- 折叠
摘要
Abstract
Using improved particle swarm optimization(PSO)to optimize the parameters of long short-term memory(LSTM),a direct drive wind turbine operation status monitoring method based on improved PSO-LSTM algorithm was proposed.Firstly,random forest method was applied to select feature from the collected supervisory control and data acquisition(SACDA)data,and the input parameters of the model were obtained.Secondly,the modified PSO-LSTM network was used to establish the active power prediction model,the residual between the predicted value and the actual value was calculated,and then the state of the direct-driven wind turbine was obtained according to the residual distribution.Finally,a wind turbine SCADA data was used to verify and analyze the proposed prediction model.The results show that compared with the other three prediction models,the PSO-LSTM prediction model can send out fault alarm in the shortest time when abnormal situation appears,achiving a higher precision and thus ensuring the healthy and stable operation of the wind farm.关键词
直驱式风力发电机/状态监测/粒子群算法/长短期记忆网络Key words
direct-drive wind turbine/condition monitoring/particle swarm optimization/long-term and short-term memory network分类
能源科技引用本文复制引用
王印松,刘佳微,贾思宇,翁疆..基于改进PSO-LSTM算法的风电机组状态监测方法研究[J].山东电力技术,2024,51(5):30-37,8.
