本文選題：風(fēng)電機組 + 工況辨識��； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：近年來,風(fēng)力發(fā)電行業(yè)的快速增長導(dǎo)致風(fēng)力發(fā)電機組故障頻發(fā),運行維護費用持續(xù)增長。加強對風(fēng)電機組運行狀態(tài)的有效監(jiān)測、實現(xiàn)風(fēng)電機組早期故障的預(yù)警和診斷對于保障機組安全穩(wěn)定運行、減少維修支出有著重要意義。與此同時,由于大型風(fēng)電機組運行工況的多變性與復(fù)雜性使得機組運行狀態(tài)難以準確評估,基于工況辨識的風(fēng)電機組在線監(jiān)測已經(jīng)逐漸成為風(fēng)電發(fā)展中的重要研究方向。本文首先對風(fēng)電機組運行工況進行分析和辨識,然后以風(fēng)電機組齒輪箱和滾動軸承為研究對象,從SCADA參數(shù)和振動參數(shù)兩種數(shù)據(jù)來源著手,分別基于多元狀態(tài)估計模型和變分模態(tài)分解方法,研究相對應(yīng)故障預(yù)警模型和故障診斷方法,具體分為以下幾個方面:首先,分析風(fēng)電機組運行特性,利用風(fēng)電場SCADA系統(tǒng)數(shù)據(jù),在進行數(shù)據(jù)分析和預(yù)處理后,選擇適當(dāng)參數(shù),利用模糊聚類算法,進行風(fēng)電機組運行子工況的劃分。其次,研究基于多元狀態(tài)估計的風(fēng)電機組故障預(yù)警。對于齒輪箱典型故障進行研究和分析;詳細介紹MSET建模原理,研究MSET建模變量選取問題,通過實際風(fēng)場數(shù)據(jù)對該模型的性能進行驗證,并通過實驗對比分析得出結(jié)論,工況辨識能夠降低異常點誤判斷率從而降低誤報警率。最后,研究基于變分模態(tài)分解的風(fēng)電機組故障診斷。針對滾動軸承早故障信號微弱難以提取出有效信息的問題,提出基于VMD、AR模型以及奇異值分解的特征提取方法;使用實際風(fēng)場數(shù)據(jù)的仿真研究中,通過與EMD分解方法進行對比,分析了VMD在抑制模態(tài)混疊方面的優(yōu)越性;針對在對振動信號進行分類時特征向量維數(shù)過高的問題,提出基于FCM和KPCA的分類方法,使用KPCA進行數(shù)據(jù)降維處理,降維處理能夠提高分類算法的有效性;最后使用凱斯實驗室數(shù)據(jù)進行實驗分析,工況的不同明顯影響著故障診斷的精度,對振動信號數(shù)據(jù)進行工況劃分,在相應(yīng)的工況下進行故障診斷可以提高故障診斷的精確性。
[Abstract]:In recent years, the rapid growth of wind power industry has led to frequent failures of wind turbines and continuous increase in operating and maintenance costs. It is of great significance to strengthen the effective monitoring of the operating state of wind turbines and to realize the early warning and diagnosis of wind turbine faults for ensuring the safe and stable operation of the units and reducing the maintenance expenses. At the same time, due to the variability and complexity of the operating conditions of large-scale wind turbines, it is difficult to accurately evaluate the operating state of wind turbines. On-line monitoring of wind turbines based on condition identification has gradually become an important research direction in the development of wind power. In this paper, the operating conditions of the wind turbine are analyzed and identified, and then the gearbox and the rolling bearing of the wind turbine are taken as the research objects, starting from the two data sources of SCADA parameters and vibration parameters. Based on the multivariate state estimation model and variational mode decomposition method, the corresponding fault warning model and fault diagnosis method are studied, which are divided into the following aspects: firstly, the operating characteristics of wind turbine are analyzed, and the data of wind farm SCADA system are used. After data analysis and preprocessing, appropriate parameters are selected and fuzzy clustering algorithm is used to partition the operating sub-conditions of wind turbine. Secondly, the wind turbine fault warning based on multivariate state estimation is studied. The typical fault of the gearbox is studied and analyzed, the principle of MSET modeling is introduced in detail, the selection of MSET modeling variables is studied, the performance of the model is verified by the actual wind field data, and the conclusion is drawn through the comparison and analysis of experiments. Condition identification can reduce the rate of misjudgment of abnormal points and thus the rate of false alarm. Finally, the fault diagnosis of wind turbine based on variational mode decomposition is studied. Aiming at the problem that it is difficult to extract effective information from the weak fault signal of rolling bearing, a feature extraction method based on VMD-AR model and singular value decomposition is proposed, which is compared with EMD decomposition method in the simulation study of actual wind field data. This paper analyzes the superiority of VMD in suppressing modal aliasing, aiming at the problem that the dimension of eigenvector is too high when classifying vibration signals, proposes a classification method based on FCM and KPCA, and uses KPCA to reduce the dimension of data. Dimensionality reduction can improve the validity of classification algorithm. Finally, case laboratory data are used for experimental analysis. Different working conditions obviously affect the accuracy of fault diagnosis, and the vibration signal data are divided into working conditions. Fault diagnosis can improve the accuracy of fault diagnosis.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM315

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