【摘要】：電力變壓器作為電力系統(tǒng)運(yùn)行的重要設(shè)備之一,為保證供電的可靠性和安全性,對運(yùn)行的變壓器進(jìn)行故障在線檢測非常必要。紅外診斷技術(shù)作為一種有效的故障檢測手段被廣泛采用。它可以檢測和診斷電力變壓器大量的內(nèi)部、外部缺陷,快速對設(shè)備熱狀態(tài)進(jìn)行紅外成像,通過對電力變壓器故障的紅外圖像的分析,從而對運(yùn)行的變壓器存在的事故隱患和缺陷進(jìn)行定位、定性的故障診斷。 在對變壓器的紅外檢測方法進(jìn)行研究,總結(jié)國內(nèi)外研究成果的基礎(chǔ)上,結(jié)合課題的實際要求,提出了紅外診斷技術(shù)對電力變壓器的故障進(jìn)行在線檢測的方案。通過設(shè)計的變壓器故障在線檢測與診斷系統(tǒng),完成變壓器故障的在線檢測的效果 首先對采集的變壓器紅外圖像進(jìn)行預(yù)處理。通過線性變換和直方圖均衡化算法,實現(xiàn)紅外圖像的圖像增強(qiáng)；然后根據(jù)紅外圖像的噪聲特點(diǎn),討論了幾種經(jīng)典的去噪算法,采用小波包閾值算法對紅外圖像進(jìn)行消噪。仿真結(jié)果顯示,該算法有效地抑制了圖像中的噪聲信號,很好的改善了圖像的質(zhì)量。其次,研究了紅外圖像分割技術(shù)中兩種經(jīng)典的分割算法,即邊緣檢測和Ostu分割法。對比實驗結(jié)果圖,采取了Canny算子邊緣檢測的圖像分割法。然后根據(jù)電力變壓器各部位圖像的特征,采用改進(jìn)的Hu不變矩提取特征值,并利用最近鄰分類器進(jìn)行圖像識別。最后,選用Visual Basic6.0設(shè)計了電力變壓器故障在線檢測與診斷系統(tǒng)。該系統(tǒng)針對某區(qū)域進(jìn)行重點(diǎn)監(jiān)測,通過故障溫度閾值與溫度變化率的雙重判斷預(yù)測設(shè)備的運(yùn)行情況；或根據(jù)“圖像特征判斷法”與“模糊溫差法”,并結(jié)合紅外圖像數(shù)據(jù)庫中的信息判斷變壓器故障的類型,基本實現(xiàn)了變壓器紅外圖像故障的在線檢測。
[Abstract]:Power transformer is one of the most important equipments in power system operation. In order to ensure the reliability and safety of power supply, it is necessary to detect the fault of the running transformer on line. Infrared diagnosis technology is widely used as an effective method of fault detection. It can detect and diagnose a large number of internal and external defects of power transformers, and fast infrared imaging of the thermal state of power transformers, through the analysis of infrared images of power transformer faults, In order to locate the hidden trouble and defect of the running transformer and diagnose the fault qualitatively. On the basis of studying the infrared detection method of transformer and summarizing the research results at home and abroad, combined with the practical requirements of the subject, the paper puts forward a scheme of on-line detection of power transformer fault by infrared diagnosis technology. Through the design of transformer fault on-line detection and diagnosis system, the effect of on-line detection of transformer fault is completed. First, the infrared image of transformer is preprocessed. Infrared image enhancement is realized by linear transformation and histogram equalization algorithm. Then according to the noise characteristics of infrared image, several classical denoising algorithms are discussed, and wavelet packet threshold algorithm is used to de-noise infrared image. Simulation results show that the algorithm can effectively suppress the noise signal in the image and improve the image quality. Secondly, two classical segmentation algorithms in infrared image segmentation, namely edge detection and Ostu segmentation, are studied. Compared with the experimental results, the image segmentation method based on Canny operator edge detection is adopted. Then, according to the features of power transformer image, the improved Hu moment invariant moment is used to extract the feature value, and the nearest neighbor classifier is used to recognize the image. Finally, the on-line fault detection and diagnosis system of power transformer is designed with Visual basic 6.0. The system focuses on monitoring a certain area and predicts the operation of the equipment by double judgment of fault temperature threshold and temperature change rate, or according to "image feature judgment method" and "fuzzy temperature difference method". Combined with the information in the infrared image database to judge the type of transformer fault, the on-line detection of transformer infrared image fault is basically realized.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張國燦;蘇東青;葉玉云;;紅外成像技術(shù)在電力設(shè)備狀態(tài)檢測中的應(yīng)用[J];電工技術(shù);2012年12期

2 陶霞;牟惠文;趙生虎;;紅外熱像技術(shù)在電氣設(shè)備故障診斷中的應(yīng)用[J];電氣開關(guān);2013年03期

3 崔昊楊;許永鵬;曾俊冬;唐忠;;電氣設(shè)備故障紅外診斷現(xiàn)狀及發(fā)展趨勢[J];上海電力學(xué)院學(xué)報;2013年03期

4 陳健;陳媛媛;;電氣設(shè)備紅外在線監(jiān)測與診斷管理研究[J];安徽電氣工程職業(yè)技術(shù)學(xué)院學(xué)報;2013年01期

5 閆光輝;楊立;范春利;;基于紅外測溫的電氣控制柜內(nèi)部元件熱缺陷溫度與方位的三維反問題識別[J];紅外與激光工程;2012年11期

6 羅云林;章翔;;基于紅外熱成像與集成SVM的航空器電子板卡故障檢測[J];測控技術(shù);2012年12期

7 戴文遠(yuǎn);;基于紅外熱圖像的故障診斷方法綜述[J];紅外;2013年02期

8 張思陽;徐敏強(qiáng);高晶波;;基于紅外熱像及反演算法的氣缸故障診斷[J];化工機(jī)械;2013年02期

9 夏清;胡振琪;位蓓蕾;王亞云;陳超;;一種新的紅外熱像儀圖像邊緣檢測方法[J];紅外與激光工程;2014年01期

10 陳仁剛;楊榮華;菅有為;崔勇;;紅外測溫技術(shù)在變壓器高壓套管故障診斷的應(yīng)用[J];山東電力技術(shù);2013年02期

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