本文選題：風電機組 + 預測與健康管理��； 參考：《電力系統(tǒng)自動化》2017年18期

【摘要】：運行工況識別作為風電機組狀態(tài)監(jiān)測與健康管理領(lǐng)域的重要環(huán)節(jié),往往受到不確定信息以及高速實時數(shù)據(jù)流的影響,造成健康狀態(tài)評估難以有效實施。在此背景下,文中提出一種基于Spark流式處理的健康狀態(tài)實時評估方法。首先,采用大數(shù)據(jù)分析技術(shù)實現(xiàn)風電機組運行工況的空間劃分;然后,在充分考慮風電機組監(jiān)測信息不確定性的情況下,結(jié)合數(shù)據(jù)采集與監(jiān)控(SCADA)歷史運行數(shù)據(jù),對基于高斯云模型和高斯云變換的健康狀態(tài)評估模型進行訓練,并以健康指數(shù)作為風電機組健康狀態(tài)評估的指標。最后,將該評估方法應用在中國北方某風電場1.5 MW風電機組故障前的健康狀態(tài)評估中。算例分析結(jié)果表明,該方法可監(jiān)測到風電機組健康狀態(tài)的變化趨勢,初步實現(xiàn)了故障的早期預警。
[Abstract]:As an important link in the field of wind turbine condition monitoring and health management, operating condition identification is often affected by uncertain information and high speed real-time data flow, which results in the difficulty of effective implementation of health status assessment. In this context, a real-time assessment method of health status based on Spark flow processing is proposed. First of all, big data analysis technology is used to realize the space division of wind turbine operating conditions, and then, with full consideration of the uncertainty of monitoring information of wind turbines, the historical operation data of SCADAs are combined with data acquisition and monitoring. The health state assessment model based on Gao Si cloud model and Gao Si cloud transformation is trained, and the health index is used as the index of wind turbine health assessment. Finally, the method is applied to evaluate the health status of a 1.5 MW wind turbine unit in northern China before failure. The result of example analysis shows that the method can monitor the change trend of wind turbine's health state and realize the early warning of failure.
【作者單位】： 華北電力大學控制與計算機工程學院;浙江大學電氣工程學院;文萊科技大學電機與電子工程系;
【基金】：國家自然科學基金資助項目(51407076) 河北省自然科學基金資助項目(F2014502050) 中央高�；究蒲袠I(yè)務費專項資金資助項目(2015ZD28)~~
【分類號】：TM315
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本文編號：1844283