【摘要】：傳統(tǒng)的風機故障預警一般通過設定單一變量的恒定預警閾值來實現,但在實際復雜工況下,這種方法易導致故障誤報、不報或預留排查時間不足。在不同的環(huán)境溫度下,風機的運行狀況不完全相同,并網發(fā)電量、并網功率、各部件溫度也不盡相同,僅依靠恒定預警值無法滿足復雜多變的工況下的預警要求。針對此問題,本文提出一種基于大數據分析的風機群落中異常風機的識別和故障預警方法。通過對河北赤沽地區(qū)某大型風電場中所有風機運行狀況的研究,并結合現場SCADA系統(tǒng)中相關歷史數據的整理分析,采用聚類分析的方法對風場中運行工況相似的風機進行群落劃分。采用統(tǒng)計學原理,對每一個群落中風機溫度類參數進行箱式分布,依據箱式圖中離群點的分布特性識別出群落中表現為離群特性的風機。在此基礎上采用顯著性差異分析方法對離群風機進行異常顯著性判斷,識別出異常運行的離群風機。為排除偶然因素造成的干擾,采用滑動窗口異常率統(tǒng)計分析方法消除風電機組奇異點的干擾,實現了風機群落中異常風機的識別。在hadoop大數據分析平臺中采用“分布式存儲、并行式計算”的方法對整個風場進行分析,實現所有群落中異常風機地識別。為進一步預測異常風機的變化特性,采用線性回歸分析方法對異常風機正常歷史數據進行建模,采用實時數據對模型進行殘差預測。結合現場經驗設置合理的預測殘差預警閾值,從而實現異常風機的故障預警。
[Abstract]:The traditional fan fault early warning is usually realized by setting a constant early warning threshold of a single variable, but under the actual complex working conditions, this method is easy to lead to false alarm of the fault, and there is not enough time to report or reserve the check. At different ambient temperatures, the operation status of the fan is not exactly the same, grid-connected power, the temperature of each component is not the same, only rely on constant early warning value can not meet the requirements of early warning under complex and changeable working conditions. In order to solve this problem, this paper presents a method of identification and fault early warning of abnormal fans in fan community based on big data analysis. Based on the study of the operation status of all the fans in a large wind farm in Chigu area of Hebei Province, and combined with the collation and analysis of the relevant historical data in the field SCADA system, the fan community with similar operating conditions in the wind field is divided by cluster analysis. Based on the statistical principle, the fan temperature parameters in each community are distributed in the box, and the fans with outlier characteristics in the community are identified according to the distribution characteristics of the outliers in the box diagram. On this basis, the significance difference analysis method is used to judge the abnormal significance of the outlier fan, and the abnormal operation of the outlier fan is identified. In order to eliminate the interference caused by accidental factors, the statistical analysis method of abnormal rate of sliding window is used to eliminate the interference of singularity of wind turbine, and the identification of abnormal fan in fan community is realized. In hadoop big data analysis platform, the method of "distributed storage and parallel calculation" is used to analyze the whole wind field, and the identification of abnormal fan in all communities is realized. In order to further predict the variation characteristics of abnormal fan, linear regression analysis method is used to model the normal history data of abnormal fan, and real-time data are used to predict the residual error of the model. Combined with the field experience, a reasonable prediction residual early warning threshold is set to realize the fault early warning of abnormal fan.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;TM614

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