發(fā)布時(shí)間：2018-07-07 21:26

  本文選題：紅外圖像 + 圖像處理��； 參考：《上海電機(jī)學(xué)院》2017年碩士論文

【摘要】：變電設(shè)備熱故障診斷對(duì)電網(wǎng)事故的預(yù)防起著非常關(guān)鍵的作用。紅外檢測(cè)技術(shù)已經(jīng)廣泛應(yīng)用于變電設(shè)備的狀態(tài)監(jiān)測(cè)與熱故障診斷,但是目前國(guó)內(nèi)仍普遍采用人員手持紅外熱像儀的方式進(jìn)行定期巡檢,效率較低,不能及時(shí)發(fā)現(xiàn)熱故障。本文對(duì)變電設(shè)備紅外圖像識(shí)別所必需的圖像去噪、圖像分割、特征提取和分類(lèi)識(shí)別方法,以及變電設(shè)備熱故障紅外診斷方法進(jìn)行研究,并將研究成果應(yīng)用于變電設(shè)備紅外圖像識(shí)別與熱故障診斷仿真系統(tǒng),為變電設(shè)備熱故障實(shí)時(shí)診斷的實(shí)現(xiàn)奠定基礎(chǔ)。首先,通過(guò)分析紅外圖像的特點(diǎn)及常見(jiàn)噪聲,針對(duì)傳統(tǒng)小波閾值去噪的不足進(jìn)行了改進(jìn),并提出中值濾波加改進(jìn)的小波閾值去噪的方法來(lái)去除變電設(shè)備紅外圖像中的混合噪聲,通過(guò)實(shí)驗(yàn)驗(yàn)證了所提出方法的有效性。其次,根據(jù)變電設(shè)備紅外圖像的特點(diǎn)提出了改進(jìn)的區(qū)域生長(zhǎng)方法,分割效果比傳統(tǒng)的區(qū)域生長(zhǎng)法更好,并結(jié)合使用邊緣檢測(cè)和數(shù)學(xué)形態(tài)學(xué)方法分割出目標(biāo)設(shè)備的邊緣。接著,針對(duì)變電設(shè)備紅外圖像獲取過(guò)程中可能會(huì)出現(xiàn)的圖像旋轉(zhuǎn)、縮放和平移現(xiàn)象,采用Hu不變矩對(duì)圖像中變電設(shè)備的形狀特征進(jìn)行提取,然后使用支持向量機(jī)(SVM)進(jìn)行變電設(shè)備紅外圖像的識(shí)別,識(shí)別的準(zhǔn)確率較高。最后,根據(jù)紅外測(cè)溫技術(shù),使用基于相對(duì)溫差法的熱故障診斷方法,通過(guò)設(shè)計(jì)仿真系統(tǒng),實(shí)現(xiàn)了變電設(shè)備紅外圖像識(shí)別與熱故障的診斷。
[Abstract]:Thermal fault diagnosis of substation equipment plays a key role in the prevention of power network accidents. Infrared detection technology has been widely used in the condition monitoring and thermal fault diagnosis of substation equipment, but at present, it is still widely used in our country to carry out regular inspection by holding infrared thermal imager. The efficiency is low, and thermal fault can not be found in time. In this paper, the necessary methods of image denoising, image segmentation, feature extraction and classification recognition for infrared image recognition of substation equipment are studied, as well as infrared diagnosis method for thermal fault of transformer equipment. The research results are applied to the infrared image recognition and thermal fault diagnosis simulation system of substation equipment, which lays a foundation for the realization of real-time thermal fault diagnosis of substation equipment. Firstly, by analyzing the characteristics of infrared image and common noise, the traditional wavelet threshold de-noising method is improved, and the median filter and improved wavelet threshold de-noising method is proposed to remove the mixed noise in the infrared image of transformer equipment. The effectiveness of the proposed method is verified by experiments. Secondly, according to the characteristics of infrared image of substation equipment, an improved region growth method is proposed. The segmentation effect is better than the traditional region growth method, and the edge of the target equipment is segmented by using edge detection and mathematical morphology. Then, aiming at the phenomenon of image rotation, scaling and translation, Hu invariant moment is used to extract the shape feature of the transformer device in the image, which may appear in the process of infrared image acquisition. Then support vector machine (SVM) is used to recognize infrared image of transformer equipment, and the recognition accuracy is high. Finally, according to the infrared temperature measurement technology, the thermal fault diagnosis method based on the relative temperature difference method is used, and the infrared image recognition and thermal fault diagnosis of the transformer equipment are realized by designing the simulation system.
【學(xué)位授予單位】：上海電機(jī)學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41;TM507

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鄒輝;黃福珍;;基于FAsT-Match算法的電力設(shè)備紅外圖像分割[J];紅外技術(shù);2016年01期

2 崔昊楊;許永鵬;孫岳;孫旭日;盛戈v，

本文編號(hào)：2106325