本文選題：電氣火花　切入點(diǎn)：小波分析　出處：《中國礦業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著現(xiàn)代工業(yè)及科學(xué)技術(shù)的迅猛發(fā)展，變配電系統(tǒng)的運(yùn)行壓力正逐漸體現(xiàn)出來，而在日益復(fù)雜化的龐大電力系統(tǒng)中，異常放電引起的電氣火花是造成系統(tǒng)故障的關(guān)鍵原因。而電氣火花產(chǎn)生的過程中伴隨著高頻脈沖電磁、光、熱等現(xiàn)象，所以其破壞性很大，不但可以造成電路、電氣設(shè)備和設(shè)施的損壞，也會(huì)引發(fā)火災(zāi)等對人員生命財(cái)產(chǎn)造成損失。據(jù)統(tǒng)計(jì)，我國的電氣火花引起的火災(zāi)所占總火災(zāi)事故的比例逐年上升，一些重大的火災(zāi)事故都是由于電氣火花導(dǎo)致。 本文首先分析了電氣火花的發(fā)生機(jī)理、分類及特征，最終確定高壓放電火花、低壓放電火花及輝光放電火花為本文的研究對象。其次，搭建了三種類型放電火花的試驗(yàn)系統(tǒng)，并通過試驗(yàn)采集了這三種類型的火花波形，進(jìn)一步確定提取5個(gè)頻率上對應(yīng)的幅值作為放電火花電磁波的特征參數(shù)，并用作后期的識(shí)別�？紤]到采集的火花波形信號(hào)存在噪聲信號(hào)的干擾影響，運(yùn)用小波分析對其進(jìn)行去噪、重構(gòu)，濾除噪聲干擾，并進(jìn)行頻譜分析，最終獲取較為精確的特征參數(shù)信息。最后，，比較分析了BP神經(jīng)網(wǎng)絡(luò)及支持向量機(jī)SVM這兩種識(shí)別方法的原理、分類規(guī)則；建立了火花識(shí)別模型，通過訓(xùn)練學(xué)習(xí)，驗(yàn)證了對電氣火花電磁波的識(shí)別工作。 實(shí)驗(yàn)結(jié)果表明，在對25組待測火花波形進(jìn)行的識(shí)別實(shí)驗(yàn)中，BP神經(jīng)網(wǎng)絡(luò)識(shí)別正確21組，錯(cuò)誤4組，準(zhǔn)確率84%，而SVM識(shí)別正確23組，錯(cuò)誤2組，準(zhǔn)確率為92%。并且SVM模型識(shí)別的時(shí)間明顯小于BP神經(jīng)網(wǎng)絡(luò)，所以，本文最終確定SVM為電氣放電火花電磁波特征識(shí)別的工具。
[Abstract]:With the rapid development of modern industry and science and technology, the operating pressure of substation and distribution system is gradually reflected, and in the increasingly complicated power system, The electrical spark caused by abnormal discharge is the key cause of system failure, and the electrical spark is accompanied by high frequency pulse electromagnetic, light, heat and other phenomena, so it is very destructive, not only can cause circuit, The damage of electrical equipment and facilities will also cause fire and other losses to people's lives and property. According to statistics, the proportion of fires caused by electrical sparks in China has increased year by year. Some major fire accidents are caused by electrical sparks. In this paper, the mechanism, classification and characteristics of electrical discharge are analyzed, and the high voltage discharge discharge, low voltage discharge discharge and glow discharge discharge are determined as the research objects. Secondly, three kinds of discharge test systems are built. The three types of sparking waveforms are collected through experiments, and the corresponding amplitudes on five frequencies are further determined as the characteristic parameters of EDM electromagnetic waves. Considering the influence of noise on the collected spark waveform signal, wavelet analysis is used to de-noise, reconstruct, filter the noise interference, and analyze the spectrum. Finally, the principle and classification rules of BP neural network and support vector machine SVM are compared and analyzed, and the spark recognition model is established. The recognition of electric spark electromagnetic wave is verified. The experimental results show that the BP neural network can recognize the correct 21 groups, the 4 wrong groups, the accuracy 84 in 25 groups of spark waveforms, while the SVM recognizes 23 groups correctly and 2 groups of errors. The accuracy is 92 and the time of SVM model recognition is obviously shorter than that of BP neural network. Therefore, this paper finally determines that SVM is the tool to identify the electromagnetic wave characteristics of EDM.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM73

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