Wind Turbine Condition Prediction Method Based on OPSO-CNN-Bi-LSTM-Attention

LIU Hongbo; LI Benxin; ZHANG Peng; WANG Xiao

doi:10.19718/j.issn.1005-2992.2025-03-0010-10

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Wind Turbine Condition Prediction Method Based on OPSO-CNN-Bi-LSTM-Attention

更新时间：2025-12-08

- Wind Turbine Condition Prediction Method Based on OPSO-CNN-Bi-LSTM-Attention
- Journal of Northeast Electric Power University Vol. 45, Issue 3, (2025)
- 作者机构：
  
  1. 现代电力系统仿真控制与绿色电能新技术教育部重点实验室（东北电力大学）,吉林,吉林,132012
  2. 中广核（兴安盟）新能源有限公司，内蒙古兴安盟,137423
- 作者简介：
- 基金信息：
- DOI：10.19718/j.issn.1005-2992.2025-03-0010-10
  CLC：
- Published Online：30 June 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
LIU Hongbo, LI Benxin, ZHANG Peng, et al. Wind Turbine Condition Prediction Method Based on OPSO-CNN-Bi-LSTM-Attention[J]. Journal of Northeast Electric Power University, 2025, 45(3).
DOI：

LIU Hongbo, LI Benxin, ZHANG Peng, et al. Wind Turbine Condition Prediction Method Based on OPSO-CNN-Bi-LSTM-Attention[J]. Journal of Northeast Electric Power University, 2025, 45(3). DOI： 10.19718/j.issn.1005-2992.2025-03-0010-10.

摘要

为准确预测风电机组的运行状态，实现风电机组故障的早期预警，降低风电场运维成本，文中提出一种基于QPSO-CNN-Bi-LSTM-Attention的风电机组状态预测方法。首先，采用核主成分分析（Kernel Principal Component Analysis

KPCA）算法提取风电机组SCADA状态监测数据中的主成分分量，计算SPE构造风电机组状态的训练集和测试集数据；其次，基于风电机组正常状态和异常状态的训练集数据优化QPS0-CNN Bi-LSTM-Attention模型的超参数，计算测试集数据的SPE数值，并通过与SPE阈值对比，实现风电机组异常状态的提前预警；最后通过内蒙古某风场1.5MW风电机组SCADA监测数据进行算例分析，验证所提方法的有效性和准确性。算例分析表明，所提方法相比CNN、LSTM和CNN-LSTM方法，可以提取隐含在非线性数据中的主成分分量，具有更好的预测性能和稳定性。

Abstract

To accurately predict the operation status of wind turbines

realize early warning of wind turbine failures

and reduce the maintenance and operation costs

this article proposes an QPSO-CNN-Bi-LSTM-Attention based wind turbine condition prediction method.Firstly

KPCA algorithm is used to extract principal components of wind turbine SCADA monitoring data

and SPE is calculated to construct training set and testing set of wind turbine condition Secondly

the hyperparameters of the QPSO-CNN-Bi-LSTM-Attention model are optimized based on the training set data of the normal and abnormal conditions of the wind turbine

and the SPE of the testing set data is calculated to realize early warning of abnormal state by contrast SPE with its threshold.Finally

the effectiveness and accuracy of the proposed method are verified by an example analysis of the SCADA monitoring data of 1.5 WM wind turbine in a wind farm in Inner Mongolia.Case studies show that the proposed method can better extract the principal components hidden in nonlinear data

and has better prediction performance and stability compared with CNN

LSTM and CNN-LSTM methods in condition prediction of wind turbines.

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