In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily re...In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.展开更多
文摘目的探究及观察白葡奈氏菌片联合吸入糖皮质激素+长效β2受体激动剂(inhaled corticosteroid+long-acting β_(2)-agonist,ICS+LABA)治疗中重度慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)急性发作的疗效及对生活质量的影响。方法将2020年6月—2021年12月山东第一医科大学附属省立医院的80例中重度COPD急性发作患者根据随机数字表法分为2组。对照组的40例采用ICS+LABA进行治疗,观察组的40例则在对照组的基础上加用白葡奈氏菌片。比较2组的COPD治疗总有效率、不良反应发生率、治疗前后的症状体征积分、疾病状态[慢性阻塞性肺疾病评分(COPD assessment test,CAT评分)]及生活质量[世界卫生组织生存质量测定量表简表(World Health Organization on quality of life brief scale,WHOQOL-BREF评分)]。结果治疗1、2周后观察组的COPD治疗总有效率显著高于对照组,差异有统计学意义(P<0.05),2组的不良反应发生率比较,差异无统计学意义(P>0.05),治疗1、2周后观察组的COPD相关症状体征积分显著低于对照组,CAT评分构成则显著优于对照组,WHOQOL-BREF评分显著高于对照组,差异有统计学意义(P<0.05)。结论白葡奈氏菌片联合ICS+LABA治疗中重度COPD急性发作的疗效较好,且可显著改善患者的生活质量。
基金This research was funded by the Basic Research Funds for Universities in Inner Mongolia Autonomous Region(No.JY20220272)the Scientific Research Program of Higher Education in InnerMongolia Autonomous Region(No.NJZZ23080)+3 种基金the Natural Science Foundation of InnerMongolia(No.2023LHMS05054)the NationalNatural Science Foundation of China(No.52176212)We are also very grateful to the Program for Innovative Research Team in Universities of InnerMongolia Autonomous Region(No.NMGIRT2213)The Central Guidance for Local Scientific and Technological Development Funding Projects(No.2022ZY0113).
文摘In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.