本文選題：電力電子電路　切入點：故障診斷　出處：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著電力電子技術(shù)得到廣泛地應(yīng)用,電力電子設(shè)備的結(jié)構(gòu)變得越來越復(fù)雜、規(guī)模越來越龐大,同時電力電子設(shè)備的故障問題也越來越突出。電力電子電路作為電力電子設(shè)備的核心組成部件,它的故障將導(dǎo)致電力電子設(shè)備甚至整個系統(tǒng)的失效,造成巨大的損失與傷害。為了保證系統(tǒng)安全可靠地運行,應(yīng)及時地對故障電路進(jìn)行有效診斷,這就使得電力電子電路故障診斷理論和方法的研究受到人們的越來越多重視。本文對電力電子電路故障診斷中故障特征提取和故障模式辨識的關(guān)鍵問題進(jìn)行研究,包括以下內(nèi)容:(1)首先對電力電子電路各故障類型進(jìn)行分析,建立其各故障形式的仿真電路模型。針對電力電子電路故障特點,考慮實際運行條件下噪聲干擾,對所有的故障信息疊加噪聲,以模擬實際仿真信號;(2)提出基于小波包變換的方法對故障信號進(jìn)行故障特征提取,首先采用小波包分解優(yōu)預(yù)測變量閾值法對信號進(jìn)行消噪預(yù)處理,再利用小波包變換能量譜提取原始故障特征向量,結(jié)合主成元分析思想,對其進(jìn)行降維處理,再一次使故障特征得到凸顯,得到新的故障特征向量;然后研究改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的故障辨識技術(shù),對故障特征向量進(jìn)行辨識處理,仿真實例驗證方法的有效性,獲得較高的診斷率;(3)考慮到小波包故障特征提取復(fù)雜程度,為進(jìn)一步提高故障特征辨識度,提出基于交叉小波變換提取故障特征向量。交叉抗噪特性使其無需進(jìn)行降噪處理且可以同時對兩信號直接分析對比得到信號之間幅值與相位關(guān)系,將它們組成原始故障特征向量;結(jié)合主成元分析得到最終故障向量;再次將新的故障特征向量輸入改進(jìn)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行故障辨識。對上述故障特征提取方法進(jìn)行分析對比,仿真實例證明所提方法的有效性且具有更高的診斷率。
[Abstract]:With the wide application of power electronics technology, the structure of power electronic equipment becomes more and more complex, and the scale of power electronic equipment becomes larger and larger. At the same time, the fault problem of power electronic equipment is more and more prominent. Power electronic circuit, as the core component of power electronic equipment, will lead to the failure of power electronic equipment or even the whole system. In order to ensure the safe and reliable operation of the system, the fault circuit should be diagnosed effectively in time. As a result, more and more attention has been paid to the theory and method of power electronic circuit fault diagnosis. In this paper, the key problems of fault feature extraction and fault mode identification in power electronic circuit fault diagnosis are studied. Including the following contents: (1) first of all, the types of power electronic circuit faults are analyzed, and the simulation circuit models of each fault form are established. According to the fault characteristics of power electronic circuits, the noise interference under the actual operating conditions is considered. For all the fault information, the noise is superimposed, and the simulation signal is simulated. (2) A method based on wavelet packet transform is proposed to extract the fault feature of the fault signal. Firstly, the wavelet packet decomposition optimal predictive variable threshold method is used to pre-process the signal noise reduction. Wavelet packet transform energy spectrum is used to extract the original fault feature vector, combined with the main component analysis idea, the dimension reduction is carried out, the fault feature is highlighted again, and a new fault feature vector is obtained. Then the fault identification technology of improved BP neural network is studied and the fault eigenvector is identified. The simulation example verifies the effectiveness of the method and obtains a high diagnostic rate. (3) considering the complexity of wavelet packet fault feature extraction. In order to further improve the identification degree of fault features, A fault feature vector extraction based on cross wavelet transform is proposed, which makes it unnecessary to do noise reduction and can directly analyze and contrast the amplitude and phase relationship between the two signals at the same time. The original fault feature vector is formed, the final fault vector is obtained by combining the main component analysis, the new fault feature vector is input into the improved BP neural network for fault identification, and the above fault feature extraction methods are analyzed and compared. Simulation examples show that the proposed method is effective and has a higher diagnostic rate.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN707

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