本文選題：電磁兼容 + 電磁干擾　； 參考：《南京師范大學(xué)》2015年碩士論文

【摘要】：隨著高速功率開關(guān)器件在電氣電子產(chǎn)品中的普遍使用,電力電子裝置的大量傳導(dǎo)性電磁干擾(EMI)問題在現(xiàn)實中不斷出現(xiàn)并未得到很好的解決,已成為現(xiàn)代電力電子技術(shù)進步的一個重要約束,使得人們開始對電磁兼容問題重新重視。因此對電力電子設(shè)備的傳導(dǎo)性電磁干擾的診斷和產(chǎn)生機理的研究具有十分重要的意義。本文根據(jù)國內(nèi)外對電磁兼容的動態(tài)和研究現(xiàn)狀,提出了基于FastICA算法和小波分析方法在電磁兼容中的應(yīng)用。首先對獨立分量分析和小波分析理論做出詳細的闡述,并且通過Matlab仿真驗證該方法的有效性。以PI (Perfomance Index)性能指標(biāo)來衡量FastICA算法的性能,并且采用Pearson(皮爾遜)相關(guān)系數(shù)的相關(guān)度來說明分離或提取結(jié)果的有效性。首先以開關(guān)電源為例,分析傳導(dǎo)電磁干擾噪聲的產(chǎn)生機理和傳播途徑,通過人工電源網(wǎng)絡(luò)(LISN)將電源線上的總噪聲提取出來,并利用硬分離網(wǎng)絡(luò)分離出共模噪聲信號和差模噪聲信號,通過皮爾遜相關(guān)性對比分析基于硬件模態(tài)分離網(wǎng)絡(luò)的模態(tài)分離和基于FastICA算法和小波分析方法結(jié)合的模態(tài)分離。結(jié)果表明該方法對傳導(dǎo)電磁干擾的模態(tài)分離是有效的,從而對電磁兼容中模態(tài)分離過程進行了簡化,大大減少了對硬件電路的依賴,降低了成本和復(fù)雜性。其次搭建了噪聲源提取模型,使用人工電源網(wǎng)絡(luò)提取出電源線上的總噪聲。文中通過實驗不僅驗證了基于FastICA算法提取噪聲源的有效性,還通過皮爾遜相關(guān)性對比分析經(jīng)過小波分析后再應(yīng)用FastICA算法對總噪聲進行源信號提取的優(yōu)勢。結(jié)果表明該方法對傳導(dǎo)電磁干擾的噪聲源提取的有效性,精確、快速地解決了以電子器件為核心的傳導(dǎo)噪聲源識別方面的問題,簡潔高效且系統(tǒng)化,更加準(zhǔn)確的判斷產(chǎn)生傳導(dǎo)噪聲信號的電子器件,能夠更有針對性的減少電磁干擾。通過這種方法,不僅滿足技術(shù)指標(biāo),更能最大程度地降低對硬件的依賴,節(jié)省經(jīng)濟成本,實現(xiàn)技術(shù)和經(jīng)濟的一體化。
[Abstract]:With the widespread use of high-speed power switch devices in electrical and electronic products, the problem of a large number of conductive electromagnetic interference (EMI) in power electronic devices has not been solved well in reality. It has become an important constraint for the progress of modern power electronics technology, which makes people begin to pay more attention to the problem of electromagnetic compatibility (EMC). Therefore, it is of great significance to study the diagnosis and generation mechanism of conductive electromagnetic interference (EMI) in power electronic equipment. According to the dynamic and research status of EMC at home and abroad, this paper presents the application of EMC based on FastICA algorithm and wavelet analysis method. The theory of independent component analysis and wavelet analysis is described in detail, and the effectiveness of the method is verified by Matlab simulation. The performance of FastICA algorithm is evaluated by Pi Perfomance Index, and the correlation degree of Pearson correlation coefficient is used to illustrate the effectiveness of the separation or extraction results. Firstly, taking switching power supply as an example, this paper analyzes the generation mechanism and propagation way of conducting electromagnetic interference noise, and extracts the total noise from power supply line by artificial power supply network (LISN). The common mode noise signal and differential mode noise signal are separated by hard separation network. The modal separation based on hardware modal separation network and the combination of FastICA algorithm and wavelet analysis method are analyzed by Pearson correlation comparison. The results show that the proposed method is effective for the mode separation of electromagnetic interference, which simplifies the mode separation process in EMC, greatly reduces the dependence on hardware circuits, and reduces the cost and complexity. Secondly, the noise source extraction model is built, and the total noise on the power line is extracted by artificial power network. The experiment not only verifies the validity of extracting noise source based on FastICA algorithm, but also uses FastICA algorithm to extract the total noise signal after wavelet analysis through Pearson correlation comparison analysis. The results show that the method is effective and efficient in the noise source extraction of conductive electromagnetic interference. It is accurate and fast to solve the problem of the identification of conductive noise sources with electronic devices as the core. It is simple, efficient and systematic. It is more accurate to judge the electronic devices which produce the conduction noise signal, which can reduce the electromagnetic interference more pertinently. This method can not only satisfy the technical index, but also reduce the dependence on hardware, save the economic cost and realize the integration of technology and economy.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN03

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本文編號：1803783