本文選題：電力電子電路 + 故障診斷��； 參考：《湖南大學(xué)》2016年碩士論文

【摘要】：隨著電力電子技術(shù)的迅猛發(fā)展,電力電子設(shè)備越來越復(fù)雜,規(guī)模也越來越大,其發(fā)生故障的概率也越來越高。電力電子電路作為該設(shè)備結(jié)構(gòu)中的重要部分,它的故障可能會導(dǎo)致整個系統(tǒng)的故障甚至是癱瘓,造成嚴(yán)重的損失。因此保證系統(tǒng)運行的可靠性與穩(wěn)定性,及時地發(fā)現(xiàn)故障與預(yù)防故障的發(fā)生就顯得尤為重要,也使得對電力電子電路進(jìn)行故障診斷與預(yù)測方法的研究得到了越來越多的重視。本文針對故障診斷步驟中的故障特征提取與故障辨識,以及電路的故障預(yù)測方法進(jìn)行了研究,包括以下內(nèi)容：采用主成分分析方法對電力電子電路的故障特征進(jìn)行提取,得到含有原始故障數(shù)據(jù)大部分信息的主成分,并將它們組合成為新的特征向量,既降低了數(shù)據(jù)維數(shù)也使特征得到了突顯。然后研究了基于Fisher判別分析法的(Fisher Discriminant Analysis,FDA)故障辨識方法,利用它對故障特征進(jìn)行識別,得到最后的故障診斷結(jié)果,還和RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行辨識的實驗結(jié)果相比較。仿真實例驗證了所提方法的有效性,診斷的準(zhǔn)確率高。考慮到電力電子電路的非線性性質(zhì),以及容差、環(huán)境等因素的影響,特征數(shù)據(jù)間關(guān)系復(fù)雜。為了提高故障特征的辨識度,采用高階累積量方法(High Order Cumulant, HOC)提取電路的故障特征,得到各樣本對應(yīng)的的峭度和偏度,并將它們組合成新的故障特征向量。再將處理得到的新的特征向量輸入FDA故障辨識方法中進(jìn)行識別,并與第二章診斷方法的準(zhǔn)確率進(jìn)行比較,仿真實例驗證了所提方法的有效性并且具有較高的診斷準(zhǔn)確率。為了預(yù)防電路故障的發(fā)生和及時地采取“預(yù)知”維修,對電力電子電路故障預(yù)測的方法進(jìn)行了研究。首先使用主成分分析與HOC對電路各種狀態(tài)下輸出電壓信號的特征數(shù)據(jù)進(jìn)行提取,再通過FDA進(jìn)行處理,將處理得到的數(shù)據(jù)構(gòu)造為一個可以反映電路健康狀態(tài)的故障指示參數(shù)。然后根據(jù)故障指示參數(shù)所顯示出的退化趨勢得到一個經(jīng)驗?zāi)Ｐ?接著研究使用粒子濾波方法進(jìn)行故障預(yù)測和剩余有用壽命的估計。該方法實施簡單,實例證明了所提方法的有效性。
[Abstract]:With the rapid development of power electronics technology, power electronic equipment is becoming more and more complex, the scale is becoming larger and larger, and the probability of failure is becoming higher and higher.Power electronic circuit as an important part of the equipment structure, its failure may lead to the whole system failure or even paralysis, resulting in serious losses.Therefore, it is very important to ensure the reliability and stability of the system, to find and prevent the fault in time, and to make more and more attention to the fault diagnosis and prediction methods of power electronic circuits.In this paper, fault feature extraction and fault identification in fault diagnosis step, and fault prediction method of circuit are studied, including the following contents: the main component analysis method is used to extract fault feature of power electronic circuit.The principal components containing most of the information of the original fault data are obtained and combined into a new feature vector which not only reduces the dimension of the data but also highlights the features.Then, the fault identification method based on Fisher discriminant analysis is studied. The fault features are identified by using the method, and the final fault diagnosis results are obtained, and the results are compared with the experimental results of RBF neural network identification.The simulation results show that the proposed method is effective and the diagnostic accuracy is high.Considering the nonlinear property of power electronic circuit and the influence of tolerance and environment, the relationship between characteristic data is complex.In order to improve the identification degree of fault features, the high order cumulant method is used to extract the fault features of the circuit, and the kurtosis and skewness of each sample are obtained, and they are combined into new fault feature vectors.Then the new eigenvector is input into the FDA fault identification method to identify, and compared with the accuracy of the second chapter of the diagnosis method. The simulation results show that the proposed method is effective and has a high diagnostic accuracy.In order to prevent the occurrence of circuit faults and adopt "predictive" maintenance in time, the method of power electronic circuit fault prediction is studied.Principal component analysis (PCA) and HOC are used to extract the characteristic data of the output voltage signal in various states of the circuit, and then processed by FDA, the processed data is constructed as a fault indicator parameter which can reflect the healthy state of the circuit.Then an empirical model is obtained according to the degradation trend shown by the fault indication parameters, and then the particle filter method is used to predict the fault and estimate the remaining useful life.The method is simple to implement, and the effectiveness of the proposed method is proved by an example.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM507
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本文編號：1752331