基于卷積神經網絡的電力巡檢絕緣子檢測研究
發(fā)布時間：2018-07-16 08:19
【摘要】：電力巡檢是保障電網安全運行不可或缺的手段,新興的無人機巡檢通過搭載的高清相機和圖傳設備可獲取大量詳實的巡檢影像。這些巡視數據僅憑人工分析和處理,工作量龐大,效率低下,存在由工作人員經驗和素質引起的偏差。而絕緣子是電力系統(tǒng)中的常見部件,由于常年暴露在外,因而故障多發(fā),嚴重威脅電網安全,需要引入智能化的識別方法自動進行故障診斷。本文結合四川省電力公司科技項目的需求,從以下幾個方面展開研究:(1)本文通過搭建和改進卷積神經網絡實現對絕緣子的檢測,解決傳統(tǒng)檢測算法魯棒性差,泛化能力不強,準確率不高等問題。首先通過研究卷積神經網絡的特點和廣泛應用,結合工程需求和硬件支持,完成對卷積神經網絡各個部件的選型和設計,搭建適宜本課題網絡模型。其次利用無人機在不同線路和時間采集玻璃和陶瓷絕緣子樣本并進行人為拓展,作為訓練樣本。然后本文選擇開源的Caffe作為工具,結合相關調參技術對網絡結構進行改進和在訓練過程中進行優(yōu)化。通過自動學習絕緣子特征的本質和分布式表達,實現在復雜航拍背景中的絕緣子檢測,訓練準確率為95%,測試準確率為92%。(2)本文結合已訓練完備的卷積神經網絡完成絕緣子自爆的識別,解決人工分析工作量大,效率低等問題。首先利用卷積神經網絡層級結構對全局和局部特征的綜合與抽象,將訓練完備的網絡模型作為絕緣子特征抽取的工具,融入自組織特征映射網絡,實現顯著性檢測的改良。其次在顯著性檢測的基礎上,快速提取絕緣子,舍棄背景,然后結合超像素分割和輪廓檢測等圖像處理方法建立絕緣子模型,提出一種針對絕緣子自爆故障的識別算法,準確率在90%以上,取代人工分析,降低憑巡檢工作人員經驗判定的風險和誤差,保障電網安全可靠運行。(3)本文對絕緣子檢測及自爆故障識別分別進行測試驗證和對比試驗。首先針對不同背景,不同種類,不同數量的情況進行了絕緣子檢測測試,并與傳統(tǒng)的DPM和基于HoG的SVM算法進行對比。同時通過可視化效果分析網絡的性能。然后對不同背景下的自爆識別算法進行了驗證。最后以工程項目為依托,簡單介紹電力巡檢絕緣子檢測系統(tǒng)平臺的構架和應用效果。經驗證,絕緣子檢測和自爆識別均達到工程要求,有效體現巡視數據的價值,提升電力巡檢的效率和智能化水平。
[Abstract]:Power inspection is an indispensable means to ensure the safe operation of the power grid. The emerging UAV patrol can obtain a large number of detailed inspection images by carrying high-definition cameras and graphic transmission equipment. These patrol data only depend on manual analysis and processing, the workload is huge, the efficiency is low, and the deviation caused by the staff's experience and quality exists. Insulator is a common component in power system. Because it is exposed all the year round, the fault is frequently occurred, which seriously threatens the security of power network, so it is necessary to introduce intelligent identification method to diagnose the fault automatically. According to the demand of Sichuan Electric Power Company's scientific and technological project, this paper studies the following aspects: (1) this paper realizes the detection of insulators by building and improving convolutional neural networks, which solves the problem of poor robustness and poor generalization ability of traditional detection algorithms. The accuracy is not high and so on. Firstly, by studying the characteristics and wide application of convolution neural network, combining with engineering demand and hardware support, the selection and design of each component of convolutional neural network are completed, and the network model suitable for this topic is built. Secondly, using UAV to collect glass and ceramic insulator samples on different lines and time, and to carry out artificial expansion, as a training sample. Then the open source Caffe is chosen as a tool to improve the network structure and optimize the training process. By automatically learning the nature and distributed expression of insulator features, the insulator detection in complex aerial photography background is realized. The accuracy of training is 95 and the accuracy of test is 92. (2) in this paper, the self-explosion identification of insulator is completed by using the fully trained convolution neural network to solve the problems of large workload and low efficiency in manual analysis. Firstly, using the hierarchical structure of convolution neural network to synthesize and abstract the global and local features, the well-trained network model is used as the tool of insulator feature extraction, and the self-organizing feature mapping network is integrated into the self-organizing feature mapping network to achieve the improvement of salience detection. Secondly, on the basis of salience detection, the insulator is quickly extracted and the background is discarded. Then the insulator model is established by combining the image processing methods such as super-pixel segmentation and contour detection, and an algorithm for identifying insulator self-detonation fault is proposed. The accuracy rate is over 90%, which replaces manual analysis, reduces the risk and error judged by the experience of inspection staff, and ensures the safe and reliable operation of power grid. (3) the insulator detection and fault identification of self-explosion are tested and compared in this paper. Firstly, the insulator detection test is carried out for different background, different kinds and different numbers of insulators, and compared with traditional DPM and SVM algorithm based on HoG. At the same time, the performance of the network is analyzed by visual effect. Then the self-detonation recognition algorithm under different background is verified. Finally, based on the project, the frame and application effect of the platform of insulator detection system for electric power inspection are briefly introduced. It is verified that both insulator detection and self-detonation identification meet the engineering requirements, effectively reflect the value of patrol data, and improve the efficiency and intelligence level of power inspection.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP183;TM755
本文編號：2125809
[Abstract]:Power inspection is an indispensable means to ensure the safe operation of the power grid. The emerging UAV patrol can obtain a large number of detailed inspection images by carrying high-definition cameras and graphic transmission equipment. These patrol data only depend on manual analysis and processing, the workload is huge, the efficiency is low, and the deviation caused by the staff's experience and quality exists. Insulator is a common component in power system. Because it is exposed all the year round, the fault is frequently occurred, which seriously threatens the security of power network, so it is necessary to introduce intelligent identification method to diagnose the fault automatically. According to the demand of Sichuan Electric Power Company's scientific and technological project, this paper studies the following aspects: (1) this paper realizes the detection of insulators by building and improving convolutional neural networks, which solves the problem of poor robustness and poor generalization ability of traditional detection algorithms. The accuracy is not high and so on. Firstly, by studying the characteristics and wide application of convolution neural network, combining with engineering demand and hardware support, the selection and design of each component of convolutional neural network are completed, and the network model suitable for this topic is built. Secondly, using UAV to collect glass and ceramic insulator samples on different lines and time, and to carry out artificial expansion, as a training sample. Then the open source Caffe is chosen as a tool to improve the network structure and optimize the training process. By automatically learning the nature and distributed expression of insulator features, the insulator detection in complex aerial photography background is realized. The accuracy of training is 95 and the accuracy of test is 92. (2) in this paper, the self-explosion identification of insulator is completed by using the fully trained convolution neural network to solve the problems of large workload and low efficiency in manual analysis. Firstly, using the hierarchical structure of convolution neural network to synthesize and abstract the global and local features, the well-trained network model is used as the tool of insulator feature extraction, and the self-organizing feature mapping network is integrated into the self-organizing feature mapping network to achieve the improvement of salience detection. Secondly, on the basis of salience detection, the insulator is quickly extracted and the background is discarded. Then the insulator model is established by combining the image processing methods such as super-pixel segmentation and contour detection, and an algorithm for identifying insulator self-detonation fault is proposed. The accuracy rate is over 90%, which replaces manual analysis, reduces the risk and error judged by the experience of inspection staff, and ensures the safe and reliable operation of power grid. (3) the insulator detection and fault identification of self-explosion are tested and compared in this paper. Firstly, the insulator detection test is carried out for different background, different kinds and different numbers of insulators, and compared with traditional DPM and SVM algorithm based on HoG. At the same time, the performance of the network is analyzed by visual effect. Then the self-detonation recognition algorithm under different background is verified. Finally, based on the project, the frame and application effect of the platform of insulator detection system for electric power inspection are briefly introduced. It is verified that both insulator detection and self-detonation identification meet the engineering requirements, effectively reflect the value of patrol data, and improve the efficiency and intelligence level of power inspection.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP183;TM755
【參考文獻】
相關期刊論文 前9條
1 李彥冬;郝宗波;雷航;;卷積神經網絡研究綜述[J];計算機應用;2016年09期
2 翟永杰;王迪;趙振兵;;基于目標建議與結構搜索的絕緣子識別方法[J];華北電力大學學報(自然科學版);2016年04期
3 趙振兵;徐磊;戚銀城;蔡銀萍;;基于Hough檢測和C-V模型的航拍絕緣子自動協(xié)同分割方法[J];儀器儀表學報;2016年02期
4 李岳云;許悅雷;馬時平;史鶴歡;;深度卷積神經網絡的顯著性檢測[J];中國圖象圖形學報;2016年01期
5 高強;陽武;李倩;;基于稀疏差異深度信念網絡的絕緣子故障識別算法[J];電測與儀表;2016年01期
6 姜浩然;金立軍;閆書佳;;航拍圖像中絕緣子的識別與故障診斷[J];機電工程;2015年02期
7 黃凱奇;任偉強;譚鐵牛;;圖像物體分類與檢測算法綜述[J];計算機學報;2014年06期
8 趙振兵;王樂;;一種航拍絕緣子串圖像自動定位方法[J];儀器儀表學報;2014年03期
9 張少平;楊忠;黃宵寧;吳懷群;顧元政;;航拍圖像中玻璃絕緣子自爆缺陷的檢測及定位[J];太赫茲科學與電子信息學報;2013年04期
，本文編號：2125809
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