本文選題：絕緣子　切入點：桿塔　出處：《華北電力大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著智能電網(wǎng)和電力系統(tǒng)自動化的發(fā)展,計算機視覺技術越來越多地應用在電力設備的智能巡檢和在線監(jiān)測中。輸電線路中的關鍵部件(絕緣子、輸電桿塔、輸電線等)是故障頻發(fā)元件,絕緣子自爆、破損、異物等故障,輸電桿塔鳥巢、異物等故障,輸電線斷股、異物、雷擊閃絡等故障嚴重威脅著輸電線路的安全可靠運行。因此,定期監(jiān)測輸電線路關鍵部件狀況,及時發(fā)現(xiàn)故障至關重要。通過對輸電線路巡檢采集的圖像數(shù)據(jù)進行分析處理,從而發(fā)現(xiàn)輸電線路故障已成為近幾年的研究熱點。本文主要圍繞航拍圖像中絕緣子、輸電桿塔、輸電線的識別及絕緣子自爆故障、輸電桿塔鳥巢故障、輸電線異物搭掛故障的檢測進行研究,論文的主要內(nèi)容如下:首先,提出了一種基于顯著性檢測與形態(tài)學的絕緣子識別及自爆故障檢測方法。利用融合多特征的顯著性算法定位絕緣子;對絕緣子定位結(jié)果區(qū)域進行OTSU二值分割提取絕緣子細節(jié),然后進行形態(tài)學處理,實現(xiàn)自爆故障檢測。其次,提出一種融合角點、直線、顏色和形狀特征的輸電桿塔識別和桿塔中鳥巢檢測方法。通過LSD線段檢測和Harris角點檢測方法分別提取圖像中的直線段和角點,通過融合處理和形態(tài)學處理后實現(xiàn)輸電桿塔初定位結(jié)果,然后通過提取HOG特征訓練SVM分類器實現(xiàn)輸電桿塔的終定位。對于輸電桿塔中的鳥巢故障,采用融合顏色特征、形狀特征實現(xiàn)準確檢測。隨后,提出一種基于直線檢測和平行性的輸電線提取和異物搭掛檢測方法。通過hough直線檢測和平行性判定提取輸電線,基于不變矩特征和adaboost算法在輸電線區(qū)域檢測是否存在搭掛的異物。最后,對本文工作進行了總結(jié),并指出了需要進一步開展的研究工作。
[Abstract]:With the development of smart grid and power system automation, computer vision technology is more and more used in intelligent inspection and on-line monitoring of power equipment. Transmission line) is a fault frequency component, insulator self-detonation, breakage, foreign body fault, transmission tower bird's nest, foreign body fault, transmission line broken wire, foreign body, lightning flashover and other faults seriously threaten the safe and reliable operation of transmission line. It is very important to monitor the condition of the key parts of transmission line regularly and find the fault in time. By analyzing and processing the image data collected by the transmission line inspection and inspection, It is found that the fault of transmission line has become a hot research topic in recent years. This paper focuses on the identification of insulators, transmission towers, transmission lines and insulator self-detonation faults, and the bird's nest fault of transmission towers. The main contents of this paper are as follows: first, A method of insulator identification and self-detonation fault detection based on salience detection and morphology is proposed. The location of insulator is based on the salience algorithm of fusion multi-feature, and the details of insulator are extracted by OTSU binary segmentation to the location result area of insulator. Then the morphological processing is carried out to realize the fault detection of self-explosion. Secondly, a fusion corner, a straight line, is proposed. The color and shape features of the transmission tower and the bird's nest detection in the tower are identified. The straight line and corner in the image are extracted by LSD line segment detection and Harris corner detection, respectively. After fusion and morphological processing, the initial location results of transmission tower are realized, and then the final location of transmission tower is realized by extracting HOG feature training SVM classifier. For the bird's nest fault in transmission tower, the fusion color feature is adopted. Then, a method of line detection and parallelism detection for power transmission line and foreign body hanging detection is proposed. The line detection and parallelism detection are used to extract transmission line by means of hough line detection and parallelism judgment. Based on the moment invariant feature and adaboost algorithm, the paper detects whether there is a hanging foreign body in the transmission line area. Finally, the work of this paper is summarized, and the further research work is pointed out.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM755

【參考文獻】

相關期刊論文 前10條

1 毛征;劉松松;張輝;孟燦;羅子安;;不同光照和姿態(tài)下的航拍車輛檢測方法[J];北京工業(yè)大學學報;2016年07期

2 陳明亮;;架空輸電線桿塔基礎問題及解決措施[J];建材與裝飾;2016年15期

3 孫實超;肖瀟;李棟;林靖宇;盧子廣;;利用參數(shù)關聯(lián)性的輸電線檢測方法[J];廣西大學學報(自然科學版);2015年06期

4 吳一全;周楊;龍云淋;;基于自適應參數(shù)支持向量機的高光譜遙感圖像小目標檢測[J];光學學報;2015年09期

5 張旭;高佼;王萬國;劉O，

本文編號：1615173