基于D-S證據融合的風力發(fā)電機組的故障預測
發(fā)布時間：2018-10-12 21:12
【摘要】：隨著不可再生能源的快速消耗,能源問題已經成為人類迫切需要解決的需求,風能因為持續(xù)可再生而成為備受注目的清潔能源。風力發(fā)電機是完成能量轉換的關鍵部件,而風力發(fā)電機的故障診斷和維護是保障風機穩(wěn)定正常運行的首要條件。風力發(fā)電機往往裝在人跡罕至的極端環(huán)境或者海平面上,傳統(tǒng)的設備維修都是等到風機損壞之后再派人過去維修,這樣不僅浪費大量的人力物力,有時候會因為風機長期帶病運行,最終造成嚴重的不可逆的設備故障,所以如何能對風機的故障進行早期預測成為一個值得研究的問題。本課題在大連駝山風場積累的歷史數(shù)據和故障日志基礎上,主要針對雙饋異步發(fā)電機的常見故障進行故障預測,待識別的風機故障包括定子繞組短路,轉子繞組短路,軸承損壞和轉子偏心,前兩個屬于電氣故障,而后兩個屬于機械故障。通過數(shù)據選取和小波包分解提取振動和電流頻域特征向量,然后通過D-S證據融合理論建立故障預測模型。傳統(tǒng)的故障診斷是通過分析正處在故障中機器運行參數(shù)建立診斷模型,因此所建立的診斷模型只適用于已經處于故障狀態(tài)的風機。本文的方法是選取風機出現(xiàn)故障一個小時之前的運行數(shù)據,此時風機雖然仍然處于運行狀態(tài),但是振動參數(shù)和電流參數(shù)已經出現(xiàn)異常,屬于帶病運行狀態(tài),提早發(fā)現(xiàn)異常就可以提前停機,防止風機持續(xù)運行造成不可逆的損壞,同時預測出的故障類型對維修人員也有較大的參考。針對傳統(tǒng)風機故障診斷中采用振動信號構造單個特征空間的故障預測的不足,本文將電流信號引入了故障預測,并引入了基于D-S證據融合的故障預測模型,首先在振動信號和電流信號上分別構造了兩個后驗概率支持向量機,將兩個支持向量機的概率輸出作為證據融合的基本概率分配,根據Dempster融合規(guī)則計算融合之后的概率分配,針對融合過程中證據之間的沖突因子太大容易導致融合失敗的問題,本文提出了用局部可信度來修正融合之前的基本概率分配,局部可信度表示支持向量機對每種故障的預測準確率,實驗證實經過局部可信度修正過基本概率分配的多個證據在融合過程中沖突因子更低,基于D-S證據融合模型相比于非融合模型對風機四種故障均有更高的預測準確率。
[Abstract]:With the rapid consumption of non-renewable energy, the energy problem has become an urgent need to be solved. Wind energy has become the focus of attention because of renewable energy. Wind turbine is the key component to complete the energy conversion, and the fault diagnosis and maintenance of wind turbine is the primary condition to ensure the stable and normal operation of wind turbine. Wind turbines are often installed in isolated extreme environments or at sea level. Traditional equipment maintenance is to wait until the fan is damaged before sending someone to repair it. This not only wastes a lot of manpower and material resources, Sometimes it is necessary to study how to predict the fault of fan in the early stage because the fan runs for a long time and finally causes the serious irreversible equipment failure. Based on the historical data and fault log accumulated in Dalian hump wind field, this paper mainly predicts the common faults of doubly-fed asynchronous generator. The fan faults to be identified include stator winding short circuit, rotor winding short circuit, and rotor winding short circuit. Bearing damage and rotor eccentricity, the first two electrical faults, the latter two mechanical faults. The eigenvector in vibration and current frequency domain is extracted by data selection and wavelet packet decomposition, and then the fault prediction model is established by D-S evidence fusion theory. The traditional fault diagnosis model is established by analyzing the operating parameters of the machine in the process of fault, so the diagnosis model is only suitable for the fan which is already in the fault state. The method of this paper is to select the operation data of the fan one hour before the failure. At this time, the fan is still in operation state, but the vibration and current parameters have been abnormal and belong to the diseased running state. Early detection of abnormal can stop the fan in advance to prevent irreversible damage caused by the continuous operation of the fan. At the same time, the predicted fault type also has a large reference for the maintenance personnel. Aiming at the shortcoming of using vibration signal to construct a single feature space for fault prediction in traditional fan fault diagnosis, the current signal is introduced into fault prediction, and a fault prediction model based on D-S evidence fusion is introduced. Firstly, two posterior probabilistic support vector machines are constructed on the vibration signal and the current signal respectively. The probability output of the two SVM is regarded as the basic probability distribution of evidence fusion, and the probability distribution after fusion is calculated according to the Dempster fusion rule. In view of the problem that the conflict factor between the evidence in the fusion process is too large to lead to the failure of fusion, this paper proposes to modify the basic probability allocation before fusion by using local credibility. The local reliability represents the prediction accuracy of each fault by support vector machine. The experimental results show that the conflict factor is lower in the fusion process when the local reliability is corrected for the basic probability allocation. Compared with the non-fusion model, D-S evidence fusion model has higher prediction accuracy for four kinds of fan faults.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315
本文編號：2267611
[Abstract]:With the rapid consumption of non-renewable energy, the energy problem has become an urgent need to be solved. Wind energy has become the focus of attention because of renewable energy. Wind turbine is the key component to complete the energy conversion, and the fault diagnosis and maintenance of wind turbine is the primary condition to ensure the stable and normal operation of wind turbine. Wind turbines are often installed in isolated extreme environments or at sea level. Traditional equipment maintenance is to wait until the fan is damaged before sending someone to repair it. This not only wastes a lot of manpower and material resources, Sometimes it is necessary to study how to predict the fault of fan in the early stage because the fan runs for a long time and finally causes the serious irreversible equipment failure. Based on the historical data and fault log accumulated in Dalian hump wind field, this paper mainly predicts the common faults of doubly-fed asynchronous generator. The fan faults to be identified include stator winding short circuit, rotor winding short circuit, and rotor winding short circuit. Bearing damage and rotor eccentricity, the first two electrical faults, the latter two mechanical faults. The eigenvector in vibration and current frequency domain is extracted by data selection and wavelet packet decomposition, and then the fault prediction model is established by D-S evidence fusion theory. The traditional fault diagnosis model is established by analyzing the operating parameters of the machine in the process of fault, so the diagnosis model is only suitable for the fan which is already in the fault state. The method of this paper is to select the operation data of the fan one hour before the failure. At this time, the fan is still in operation state, but the vibration and current parameters have been abnormal and belong to the diseased running state. Early detection of abnormal can stop the fan in advance to prevent irreversible damage caused by the continuous operation of the fan. At the same time, the predicted fault type also has a large reference for the maintenance personnel. Aiming at the shortcoming of using vibration signal to construct a single feature space for fault prediction in traditional fan fault diagnosis, the current signal is introduced into fault prediction, and a fault prediction model based on D-S evidence fusion is introduced. Firstly, two posterior probabilistic support vector machines are constructed on the vibration signal and the current signal respectively. The probability output of the two SVM is regarded as the basic probability distribution of evidence fusion, and the probability distribution after fusion is calculated according to the Dempster fusion rule. In view of the problem that the conflict factor between the evidence in the fusion process is too large to lead to the failure of fusion, this paper proposes to modify the basic probability allocation before fusion by using local credibility. The local reliability represents the prediction accuracy of each fault by support vector machine. The experimental results show that the conflict factor is lower in the fusion process when the local reliability is corrected for the basic probability allocation. Compared with the non-fusion model, D-S evidence fusion model has higher prediction accuracy for four kinds of fan faults.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315
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