發(fā)布時間：2019-05-11 20:44

【摘要】：隨著電力電子技術(shù)的發(fā)展,電力變換裝置在工業(yè)應(yīng)用中所占據(jù)的地位越來越重,一旦發(fā)生故障而不能及時診斷與恢復(fù),會帶來巨大的經(jīng)濟(jì)損失。為保證電力變換裝置安全可靠運(yùn)行,研究電力電子電路的故障診斷方法具有重要的意義。本文以鍵合圖理論為基礎(chǔ),研究基于鍵合圖模型下的定量、定性及支持向量機(jī)的故障診斷方法,主要內(nèi)容有:(1)研究基于定量鍵合圖的故障診斷方法。首先根據(jù)系統(tǒng)的鍵合圖模型,建立解析冗余關(guān)系與故障特征矩陣。然后分析傳感器輸出信號得到殘差趨勢,將殘差時域響應(yīng)與閾值比較生成系統(tǒng)的二進(jìn)制一致性向量,并利用故障特征矩陣實現(xiàn)系統(tǒng)的故障檢測與隔離。針對固定閾值和參數(shù)故障不可隔離的問題分別采用區(qū)間估計法與粒子群算法。最后以閉環(huán)控制的Buck電路驅(qū)動直流電機(jī)為例,對該方法進(jìn)行驗證,實驗結(jié)果表明該法是正確有效的。(2)研究基于定性鍵合圖的故障診斷方法。首先討論了鍵合圖元件的因果關(guān)系、系統(tǒng)時間因果圖的生成、故障樹的建立及定位故障的過程。然后以汽車牽引系統(tǒng)為機(jī)械部分的機(jī)電系統(tǒng)為例,建立系統(tǒng)的鍵合圖模型,并分析各鍵合圖元件的因果關(guān)系生成時間因果圖,在此基礎(chǔ)上推導(dǎo)得到故障樹,采用逆向逐層推理法,定位出引起故障發(fā)生的故障源。最后通過仿真和物理實驗對其進(jìn)行驗證,實驗結(jié)果分析表明該方法能有效識別故障位置。(3)研究組合型變流器的故障診斷。首先對支持向量機(jī)與近似熵的理論做了簡要的介紹。然后以AC/DC-DC/AC變流器為例,分析逆變電路典型的4種故障情形下的電流信號輸出。最后對變流器故障情況仿真,采用經(jīng)驗?zāi)�態(tài)分解法對三相輸出電流信號進(jìn)行分解,計算分解得到IMF的能量熵輸入支持向量機(jī),同時對支持向量機(jī)的參數(shù)采用遺傳算法優(yōu)化,結(jié)果顯示該法對于變流器的五類故障問題具有良好診斷效果。
[Abstract]:With the development of power electronics technology, the power conversion device occupies more and more important position in the industrial application. Once the fault occurs and can not be diagnosed and restored in time, it will bring huge economic losses. In order to ensure the safe and reliable operation of power conversion device, it is of great significance to study the fault diagnosis method of power electronic circuit. Based on the theory of bond graph, this paper studies the fault diagnosis methods of quantitative, qualitative and support vector machine based on bond graph model. The main contents are as follows: (1) the fault diagnosis method based on quantitative bond graph is studied. Firstly, according to the bond graph model of the system, the analytical redundancy relation and fault characteristic matrix are established. Then the residual trend of the sensor output signal is analyzed, the residual time domain response and the threshold are compared to generate the binary consistency vector of the system, and the fault detection and isolation of the system is realized by using the fault characteristic matrix. Interval estimation method and particle swarm optimization algorithm are used to solve the problem of non-isolation of fixed threshold and parameter faults, respectively. Finally, taking the closed-loop control Buck circuit-driven DC motor as an example, the experimental results show that the method is correct and effective. (2) the fault diagnosis method based on qualitative bond graph is studied. Firstly, the causality of bond graph elements, the generation of system time causality diagram, the establishment of fault tree and the process of fault location are discussed. Then, taking the mechanical and electrical system of automobile traction system as an example, the bond graph model of the system is established, and the causality generation time causality diagram of each bond graph element is analyzed, on the basis of which the fault tree is derived. The reverse layer-by-layer reasoning method is used to locate the fault source. Finally, the simulation and physical experiments are used to verify the fault location. The experimental results show that the method can effectively identify the fault location. (3) the fault diagnosis of the combined converter is studied. Firstly, the theory of support vector machine and approximate entropy is briefly introduced. Then, taking AC/DC-DC/AC converter as an example, the output of current signal in four typical fault cases of inverter circuit is analyzed. Finally, the fault situation of converter is simulated, and the three-phase output current signal is decomposed by empirical mode decomposition method, and the energy entropy input support vector machine of IMF is obtained by calculation and decomposition. at the same time, the parameters of support vector machine are optimized by genetic algorithm. The results show that this method has a good diagnosis effect for five kinds of fault problems of converters.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN710

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本文編號：2474836