本文選題：風力發(fā)電機　切入點：復雜工況　出處：《沈陽工業(yè)大學》2014年博士論文

【摘要】：近年來，全球風電產(chǎn)業(yè)迅猛發(fā)展，然而風力發(fā)電機組的維護成本一直居高不下，這嚴重制約了風電產(chǎn)業(yè)的健康發(fā)展。風力發(fā)電機工作環(huán)境極為復雜，受風速波動、負載變化等影響，其振動信號具有非平穩(wěn)、非線性、時變等特點。傳統(tǒng)的故障診斷方法，極少考慮風力發(fā)電機的復雜工況條件對其動態(tài)特征的影響，針對這一問題，本文提出基于狀態(tài)變化過程和多傳感器融合的復雜工況下風力發(fā)電機組關鍵部件故障定量分析與診斷方法。 針對風力發(fā)電機組關鍵部件故障定量分析與診斷問題，提出一種基于Hilbert-Huang變換和信息熵的故障定量分析與診斷方法——Hilbert空間特征熵方法，首先應用Hilbert-Huang變換方法對信號時頻空間進行劃分，進而對得到的信號在瞬時時頻空間上的能量分布矩陣做奇異值分解，最后定義了信號在瞬時時頻劃分下的Hilbert空間特征熵。此外，為提高時頻空間劃分的精度，提出了一種改進Hilbert-Huang變換端點效應問題的自適應算法。為驗證該算法，利用轉(zhuǎn)子實驗臺設計不平衡-碰摩、松動-碰摩兩種常見的轉(zhuǎn)子碰摩耦合故障實驗，采集了不同轉(zhuǎn)速下轉(zhuǎn)子故障信號，，應用Hilbert空間特征熵分析測試數(shù)據(jù)，用故障信號熵值隨轉(zhuǎn)速變化的熵值曲線來描述轉(zhuǎn)子故障的程度和類型，實現(xiàn)了對轉(zhuǎn)子碰摩耦合故障的定量分析與診斷。 針對復雜工況下的風力發(fā)電機組關鍵部件故障診斷問題，首先對不同工況下風力發(fā)電機傳動系統(tǒng)進行了振動分析。分析了風力發(fā)電機控制策略對其振動的影響，進而對不同風速、不同負載條件下風力發(fā)電機軸承振動信號進行了分析，從時域、頻域、時頻域、Hilbert空間特征熵等多個角度對不同工況下風力發(fā)電機軸承的振動信號特征進行了研究，總結(jié)了風力發(fā)電機振動信號隨風速、負載變化的規(guī)律。 在此基礎上，分別對風力發(fā)電機中的軸承故障和齒輪箱故障的程度和狀態(tài)問題進行了研究。給出了考慮風速影響的軸承振動模型，從時域及時頻域的角度，對不同風速下，正常軸承和故障軸承振動信號進行了比對分析。應用Hilbert空間特征熵對不同風速下的軸承振動信號進行分析，通過比較正常軸承與故障軸承振動信號Hilbert空間特征熵值隨風速變化的曲線，可以直觀的判斷出軸承故障。進而應用Hilbert空間特征熵方法對軸承故障前一個月的在線監(jiān)測數(shù)據(jù)進行分析，結(jié)果表明，該法能有效的定量描述軸承故障程度變化的過程，并能根據(jù)其熵值突變的時間點，較早的發(fā)現(xiàn)風力發(fā)電機軸承故障。給出了風力發(fā)電機齒輪箱中各級傳動嚙合頻率及各齒輪特征頻率的計算方法，應用嚙合頻率分析方法對齒輪箱正常信號及故障信號進行了分析，結(jié)果表明，該方法雖然能有效分析出齒輪箱故障原因，但無法反映故障的程度，且診斷的結(jié)果不直觀，其過程也較為繁瑣。為更全面的反映齒輪箱的運行狀態(tài)，應用Hilbert空間特征熵方法對齒輪箱多測點、多轉(zhuǎn)速、多故障狀態(tài)下的振動信號進行融合分析。從而得到了齒輪箱振動信號Hilbert空間特征熵值隨測點位置、轉(zhuǎn)速變化的熵值平面，通過對比正常齒輪箱與故障齒輪箱的熵值平面，可以直觀的診斷出齒輪箱故障。通過對比連續(xù)離線測試獲得的故障齒輪箱熵值平面，表明通過該方法可以定量描述齒輪箱故障程度和狀態(tài)的變化。
[Abstract]:In recent years, rapid development of global wind power industry, but the maintenance cost of the wind turbine has been high, which seriously restricts the healthy development of wind power industry. The wind turbine working environment is very complex, affected by the fluctuation of wind speed, the load changes, the vibration signal is non-stationary, nonlinear, time-varying characteristics of fault. The traditional diagnostic methods, rarely take into account the influence of complex working conditions of wind turbines on the dynamic characteristics, in order to solve this problem, this paper based on the complicated working state change process and multi sensor fusion under the key components of wind turbine fault diagnosis and quantitative analysis method.
Aiming at the problem of quantitative analysis and fault diagnosis of the key components of wind turbine, proposed a Hilbert spatial entropy method based on quantitative analysis and fault diagnosis method of Hilbert-Huang transform and information entropy, the first application of Hilbert-Huang transform method of signal in time-frequency space division, and the energy distribution of the signal matrix in the instantaneous time-frequency space the singular value decomposition, finally defines the spatial features of Hilbert signal in time and frequency division instantaneous entropy condition. In addition, in order to improve the time-frequency space division accuracy, proposed an improved Hilbert-Huang transform to the end effect problem of adaptive algorithm. In order to validate the algorithm, using the design of rotor experimental platform of unbalance rubbing, loosening two kinds of rubbing rotor rubbing coupling faults of rotor fault signal acquisition experiment, different speed, entropy feature of the application of Hilbert spatial analysis test number According to the entropy value curve of the fault signal entropy and the speed change, it describes the degree and type of the rotor fault, and realizes the quantitative analysis and diagnosis of the rotor rub impact coupling fault.
The problem of fault diagnosis for the key components of wind turbine under complicated working conditions, the different conditions of wind turbine drive system was analyzed. The vibration analysis of wind turbine control strategy influence on its vibration, and the different wind speed, wind turbine bearing vibration signal under different load conditions are analyzed from time domain, frequency domain, time the frequency domain characteristic of vibration signals of multi angle Hilbert space characteristic entropy on different working conditions of wind turbine bearings were studied, summarized the wind turbine vibration signal of wind speed and load changes.
On this basis, the degree and status of fault bearing fault and gear box of wind turbine are studied. Given the bearing vibration model considering the influence of wind speed, time from time domain frequency domain, the different wind speed, normal bearing and bearing fault vibration signals were analyzed. The bearing vibration signal entropy the application of Hilbert spatial characteristics under different wind speeds were analyzed by comparing the normal bearing and the fault bearing vibration signal Hilbert spatial entropy change with wind speed curve, we can judge the bearing fault. Then using Hilbert spatial entropy method of on-line monitoring data a month before the bearing fault is analyzed, the results show that method can describe the change process of bearing fault degree effective quantitative, and according to the entropy point in the time, found that the wind turbine bearing earlier The fault is given. Calculation method of meshing frequency levels of transmission gearbox of wind turbine and the characteristic frequency of the gear, using the meshing frequency analysis method, the gear box of normal signals and fault signals. The results show that the method can effectively analyze the reasons for the failure of gear box, but can not reflect the degree of fault diagnosis, and the result is not intuitive, the process is more complicated. In order to reflect the operation state of gear box is more comprehensive, the application of Hilbert spatial entropy method for multi point measurement, the multi speed gear box, vibration signal of multi fault state of fusion analysis. To obtain the vibration signal of the gear box with the Hilbert space characteristic entropy measurement locations entropy plane speed changes, through the entropy plane compared with normal gear box and gearbox fault, can diagnose the fault of gearbox. By comparing the offline continuous test by The entropy value plane of the fault gear box is obtained, which shows that this method can quantitatively describe the change of the gear box fault degree and state.

【學位授予單位】：沈陽工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM315

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