计量学报2024，Vol.45Issue(9)：1384-1393,10.DOI:10.3969/j.issn.1000-1158.2024.09.17
基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究
Research on Early Warning of Abnormal Working Conditions of Wind Turbine Based on QM-DBSCAN and BiLSTM
摘要
Abstract
A wind turbine fault warning method based on quartile method(QM)-density-based spatial clustering of applications with noise(DBSCAN)and Bi-directional long and short-term memory network(BiLSTM)is proposed.Firstly,in view of the difficulty of cleaning the power limit point in the wind speed-power diagram,the combination of QM and DBSCAN is proposed to preprocess the modeling operation data.secondly,by analyzing the operation principle of wind turbine and determining the input and output parameters of the normal working condition prediction model of wind turbine combined with LightGBM feature selection method,a high-precision normal performance prediction model of wind turbine is established based on BiLSTM.Then,the state performance index of the fan is determined by the sliding window algorithm,and the index threshold is determined by statistical interval estimation method.Finally,the real fault data of the fan is used to carry out the early warning experiment of the abnormal working condition of the whole wind turbine,which verifies the effectiveness of the method.关键词
电学计量/风电机组/故障预警/四分位法/DBSCAN/BiLSTM/滑窗算法Key words
electrical measurement/wind turbine/fault warning/quartile method/DBSCAN/BiLSTM/sliding window algorithm引用本文复制引用
马良玉,梁书源,程东炎,耿妍竹,段新会..基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究[J].计量学报,2024,45(9):1384-1393,10.基金项目
国家自然科学基金(61973117) （61973117）
河北省中央引导地方科技发展资金(226Z2103G) （226Z2103G）
