本文選題：電力設(shè)備 + 載流故障��； 參考：《浙江大學(xué)》2014年博士論文

【摘要】：隨著電力系統(tǒng)向大容量、超高壓、遠(yuǎn)距離的方向飛速發(fā)展,電力設(shè)備的安全可靠運(yùn)行顯得越來越重要。因此,有關(guān)電力設(shè)備故障診斷技術(shù)的研究與應(yīng)用具有重要的現(xiàn)實(shí)意義。怎樣提高故障診斷系統(tǒng)的準(zhǔn)確性、快速性,使得系統(tǒng)能夠快速有效地發(fā)現(xiàn)故障、定位故障、分析故障,是電網(wǎng)保護(hù)的重要課題。 本文針對電力設(shè)備的載流故障,在總結(jié)現(xiàn)有的故障診斷技術(shù)的特點(diǎn)及應(yīng)用情況的基礎(chǔ)上,致力于故障預(yù)警及預(yù)測,旨在故障發(fā)展初期能夠發(fā)現(xiàn)故障和預(yù)測故障的發(fā)展趨勢。從機(jī)理分析、系統(tǒng)建模、數(shù)據(jù)挖掘等幾個方面進(jìn)行研究,提出了一套電力設(shè)備載流故障在線診斷算法,實(shí)現(xiàn)了“溫度采集-信號傳輸-數(shù)據(jù)分析-故障預(yù)警-故障預(yù)測”一整套流程。與現(xiàn)有診斷系統(tǒng)相比,本文提高了報警準(zhǔn)確率、提前報警時間,增加故障預(yù)測功能,使得系統(tǒng)在可靠性、快速性及靈敏性上有了顯著的提高。基于某真實(shí)110kV變電站的應(yīng)用結(jié)果表明了所提方案的有效性。 論文的主要內(nèi)容包括： (1)故障的早期預(yù)警。對主成分分析法(PCA)、K-means聚類算法和小波閾值去噪方法進(jìn)行了簡單的介紹。針對載流故障形態(tài)多樣性的特點(diǎn),采用上述方法對實(shí)時數(shù)據(jù)和歷史數(shù)據(jù)進(jìn)行綜合分析,研究提出故障特征提取算法和故障識別方法；并通過對鄰近位置的相關(guān)比較分析,研究提出載流故障的在線早期預(yù)警方法�；赑CA的預(yù)警方法的實(shí)驗(yàn)結(jié)果表明,此方法能夠準(zhǔn)確快速地預(yù)報設(shè)備故障,并將報警時間大大的提前,具有較高的可靠性和靈敏性。 (2)故障的機(jī)理建模。研究分析了載流故障的發(fā)展機(jī)理,從故障觸點(diǎn)發(fā)熱的根本原因出發(fā),掌握其溫度變化的規(guī)律。基于傳熱學(xué)原理建立了故障觸點(diǎn)的溫度模型,并利用最小二乘求得溫度模型參數(shù)。隨后基于溫度模型,實(shí)現(xiàn)了觸點(diǎn)溫度擬合、插值以及預(yù)測的功能。利用真實(shí)溫度數(shù)據(jù)驗(yàn)證所建立的溫度模型,結(jié)果表明溫度模型能夠較準(zhǔn)確地描述觸點(diǎn)溫度變化,與實(shí)際情況吻合。 (3)故障的趨勢預(yù)測。對貝葉斯濾波、蒙特卡羅方法、粒子濾波方法進(jìn)行了詳細(xì)的介紹,提出了基于粒子濾波的觸點(diǎn)溫度預(yù)測方法。該方法將求和運(yùn)算代替積分運(yùn)算,修正溫度模型參數(shù),利用修正后的參數(shù)代入溫度模型,從而進(jìn)行觸點(diǎn)溫度預(yù)測。每經(jīng)過一次采樣周期,基于更新的溫度做一次參數(shù)修正,從而得到一條動態(tài)變化的預(yù)測曲線。應(yīng)用結(jié)果表明,基于粒子濾波方法的預(yù)測方法能夠準(zhǔn)確地揭示觸點(diǎn)溫度未來的發(fā)展趨勢,并且隨著修正次數(shù)的增加,預(yù)測曲線與實(shí)際曲線逐漸趨向于一致。 (4)介紹了某真實(shí)110kV變電站的基本構(gòu)成,并詳細(xì)闡述了本文所提方法應(yīng)用到此變電站的流程。從溫度數(shù)據(jù)的獲取、溫度數(shù)據(jù)的分類、溫度數(shù)據(jù)的分析、故障預(yù)警及故障預(yù)測,應(yīng)用效果證明了本文所提方法的有效性。
[Abstract]:With the rapid development of power system in the direction of large capacity , ultra high voltage and long distance , the safe and reliable operation of power equipment is more and more important . Therefore , the research and application of fault diagnosis technology of power equipment is of great practical significance . How to improve the accuracy and speediness of the fault diagnosis system can make the system quickly and effectively detect faults , locate faults and analyze faults , which is an important subject of power grid protection .

On the basis of summarizing the characteristics and application of the existing fault diagnosis technology , this paper is devoted to the fault early warning and prediction based on summarizing the characteristics and application of the existing fault diagnosis technology .

The main contents of the thesis include :

( 1 ) In the early warning of fault , a simple introduction to PCA , K - means clustering algorithm and wavelet threshold denoising method are introduced . In view of the characteristics of the diversity of current - carrying faults , the real - time data and historical data are comprehensively analyzed by the above - mentioned method , and fault feature extraction algorithm and fault identification method are proposed .
On the basis of PCA , the results show that this method can predict the fault of equipment accurately and quickly and advance the alarm time greatly , and has higher reliability and sensitivity .

( 2 ) The mechanism of fault is modeled . The mechanism of current fault is analyzed . The temperature model of the fault contact is established based on the principle of heat transfer . The temperature model of the fault contact is established based on the principle of heat transfer , and the function of fitting , interpolation and prediction of the contact temperature is realized based on the temperature model . The temperature model is verified by using the real temperature data . The results show that the temperature model can accurately describe the temperature change of the contact points and agree with the actual situation .

( 3 ) The trend prediction of the fault is introduced . The method of Bayesian filtering , Monte Carlo method and particle filtering is introduced in detail . The method is used to replace the integral operation , the temperature model parameter is corrected , and the temperature model is modified by using the modified parameter , so as to obtain a dynamic dynamic prediction curve . The application results show that the prediction method based on the particle filter method can accurately reveal the development trend of the future of the contact temperature , and the prediction curve and the actual curve tend to be consistent with the increase of the correction times .

( 4 ) The basic structure of a real 110kV transformer substation is introduced , and the process of applying the method to this substation is described in detail . From temperature data acquisition , temperature data classification , temperature data analysis , fault early warning and fault prediction , the effectiveness of the proposed method is proved .
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM507

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