摘要
针对风机状态预测中特征提取不充分及单一模型预测精度不足的问题,提出一种融合CatBoost算法与长短期记忆网络(LSTM)的风机运行状态预测方法。首先,基于风机传感器特征和时序特征,使用SVFE和MVFE方法交叉融合生成全局复合特征,并结合熵权法改进的灰色关联分析实现特征降维。其次,通过引入混沌映射改进的麻雀搜索算法(CSSA)对LSTM模型超参数进行全局寻优,实现最优参数组合的自适应筛选与精准确定。最后,通过最优加权组合策略对CatBoost与优化后的LSTM进行深度融合,以提升预测精度与模型泛化能力。以中国宜昌某磷化工企业风机为例,对所提CSSA-CatBoost-LSTM风机状态预测方法进行了验证,验证结果表明该方法在准确性和可靠性方面有显著提升。
To address the issues of insufficient feature extraction and inadequate prediction accuracy of single models in wind turbine condition forecasting,this study proposes a wind turbine operational condition prediction method that integrates the CatBoost algorithm with the Long Short-Term Memory network(LSTM).Firstly,based on the wind turbine sensor features and temporal features,the SVFE(a feature extraction method,assume its full name is known in the specific context)and MVFE(another feature extraction method,assume its full name is known in the specific context)methods are employed for cross-fusion to generate global composite features.Additionally,feature dimension reduction is achieved by incorporating grey relational analysis improved with the entropy weight method.Secondly,the Sparrow Search Algorithm(SSA)enhanced by chaotic mapping,termed CSSA,is introduced to conduct global optimization of the hyperparameters of the LSTM model,enabling adaptive screening and precise determination of the optimal parameter combination.Finally,the CatBoost model and the optimized LSTM model are deeply fused using an optimal weighted combination strategy to enhance prediction accuracy and model generalization capability.Taking the wind turbines of a phosphorus chemical enterprise in Yichang,China,as an example,the proposed CSSA-CatBoost-LSTM wind turbine condition prediction method was validated.The validation results demonstrate significant improvements in both accuracy and reliability of this method.
作者
孙家栋
李子恒
陈德基
施珮
Sun Jiadong;Li Ziheng;Chen Deji;Shi Pei(Excellent Science and Technology Innovation Teams of Jiangsu Universities(Real-time Industrial Internet of Things),Wuxi University,Wuxi 214105,China;School of Computer Science,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《电子测量技术》
北大核心
2025年第24期167-176,共10页
Electronic Measurement Technology
基金
国家自然科学基金(62072216)
江苏省高校自然科学研究面上项目(21KJB520020)
无锡学院引进人才科研启项经费(2023r005)项目资助。
关键词
CatBoost
长短期记忆网络
熵权法
灰色关联分析方法
混沌映射
麻雀搜索算法
CatBoost
long short-term memory network
entropy weight method
grey correlation analysis method
sparrow search algorithm
chaotic mapping
