基于聲音特征的變電站電力變壓器故障檢測
發(fā)布時間：2019-04-19 15:22
【摘要】：變電站電力變壓器的故障檢測技術(shù),是指通過監(jiān)控變壓器運行狀態(tài)來檢測變電站電力變壓器是否仍處于正常工作中,若發(fā)生故障,能夠做到及時報警,方便工作人員對其進行檢測維修,同時還可以預(yù)測變壓器未來一段時間內(nèi)的工作情況。傳統(tǒng)的變壓器故障檢測方法都是依靠人工完成的,包括狀態(tài)檢修、定期檢修等,這些方法均需要相關(guān)工作人員定期在現(xiàn)場進行操作,對工作人員的技術(shù)水平以及工作經(jīng)驗都有要求,但是傳統(tǒng)檢測方法不但缺乏時效性,而且具有一定的安全風險。隨著工業(yè)發(fā)展、科技進步、人們生活水平的不斷提高,隨之而來的是用電需求大幅增長、電網(wǎng)規(guī)模變大大,傳統(tǒng)的檢測手段已無法滿足日益變化的需求,此時,伴隨著計算機與電子技術(shù)的進步,實時的在線故障檢測技術(shù)已逐步發(fā)展起來。本文提出的變電站電力變壓器故障檢測方案是通過分析、提取變壓器所發(fā)聲音的幅頻特征,并結(jié)合相應(yīng)的檢測算法來達到變壓器故障檢測檢測目的。基于聲音信號的故障檢測技術(shù)至今已發(fā)展了幾十年,六十年代主要應(yīng)用在核電、航空航天等高尖行業(yè);七十年代發(fā)展至船舶、石化、冶金等行業(yè);八十年代起逐步向各行業(yè)迅速擴展。在對變壓器進行檢測維修時,相關(guān)的工作人員可以憑借所聽到的變壓器運行聲,進而知曉變壓器是否發(fā)生了故障,本論文就是依據(jù)這種維修人員依靠聲音判斷變壓器運行狀態(tài)的方式,提出了一種模擬人類聽覺系統(tǒng)的變壓器故障檢測方案,本方案可作為變壓器故障檢測的有效輔助手段。在本方案中,首先對變壓器工作時發(fā)出的聲音進行采集,然后通過檢測系統(tǒng)對這些聲音進行判斷,進而達到對變壓器運行情況檢測的目的。在實驗過程中,本實驗組成員通過實地采集的方式對變壓器所發(fā)出的各種聲音進行收集,并建立相應(yīng)的聲音樣本庫。通過對這些聲音樣本進行分析、研究、統(tǒng)計,設(shè)計相應(yīng)的算法,進而構(gòu)建出變電站電力變壓器的故障檢測系統(tǒng)。本論文介紹了一種基于變壓器聲音數(shù)據(jù)幅頻特性的特征提取的方法,并將所提取的每個聲音數(shù)據(jù)特征組成相應(yīng)的一維向量或二維矩陣,隨后應(yīng)用主分量分析(PCA)和二維主分量分析(2DPCA)算法分別對聲音數(shù)據(jù)的頻譜特征進行降維處理,提取主要特征信息,然后應(yīng)用支持向量機(SVM)算法對聲音信號進行分類,從而達到對變電站電力變壓器狀態(tài)檢測的目的。
[Abstract]:The fault detection technology of substation power transformer means that it can detect whether substation power transformer is still in normal work by monitoring the operation status of transformer, and in case of failure, it can make timely alarm. It is convenient for the staff to test and repair the transformer, and it can also predict the future work of the transformer in a period of time. The traditional methods of transformer fault detection are completed manually, including condition-based maintenance, regular maintenance and so on. These methods require the relevant staff to operate regularly on the spot. However, traditional detection methods are not only lack of timeliness, but also have certain safety risks. With the development of industry, the progress of science and technology, and the continuous improvement of people's living standards, the demand for electricity and the scale of the power grid have been greatly increased, and the traditional means of detection have been unable to meet the ever-changing needs, at this time, With the progress of computer and electronic technology, real-time on-line fault detection technology has been gradually developed. The fault detection scheme proposed in this paper is to extract the amplitude-frequency characteristics of the sound generated by the transformer and combine with the corresponding detection algorithm to achieve the purpose of transformer fault detection by analyzing and extracting the amplitude-frequency characteristics of the sound produced by the transformer. The fault detection technology based on sound signal has been developed for decades, mainly used in nuclear power, aerospace and other advanced industries in the 1960s, in the seventies to ship, petrochemical, metallurgy and other industries; Since the 1980s, it has gradually expanded rapidly to various industries. During the inspection and maintenance of the transformer, the relevant staff member can rely on the sound heard in the operation of the transformer, and then know whether the transformer has broken down or not. In this paper, a transformer fault detection scheme simulating human auditory system is proposed according to the way that the maintenance personnel rely on sound to judge the transformer operation state. This scheme can be used as an effective auxiliary method for transformer fault detection. In this scheme, firstly, the sound emitted by the transformer is collected, then these sounds are judged by the detection system, and then the purpose of detecting the transformer operation is achieved. During the experiment, all kinds of sound emitted by the transformer were collected by the members of the experimental group, and the corresponding sound sample database was set up. Through the analysis, research, statistics and design of the corresponding algorithm, the fault detection system of substation power transformer is constructed. In this paper, a method of feature extraction based on the amplitude-frequency characteristics of transformer sound data is introduced, and each feature of the extracted sound data is composed of the corresponding one-dimensional vector or two-dimensional matrix. Then, principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) algorithm should be used to reduce the dimension of the spectral features of the sound data, extract the main feature information, and then use the support vector machine (SVM) algorithm to classify the sound signals. In order to achieve the substation power transformer status detection purpose.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM63;TM41
本文編號：2461064
[Abstract]:The fault detection technology of substation power transformer means that it can detect whether substation power transformer is still in normal work by monitoring the operation status of transformer, and in case of failure, it can make timely alarm. It is convenient for the staff to test and repair the transformer, and it can also predict the future work of the transformer in a period of time. The traditional methods of transformer fault detection are completed manually, including condition-based maintenance, regular maintenance and so on. These methods require the relevant staff to operate regularly on the spot. However, traditional detection methods are not only lack of timeliness, but also have certain safety risks. With the development of industry, the progress of science and technology, and the continuous improvement of people's living standards, the demand for electricity and the scale of the power grid have been greatly increased, and the traditional means of detection have been unable to meet the ever-changing needs, at this time, With the progress of computer and electronic technology, real-time on-line fault detection technology has been gradually developed. The fault detection scheme proposed in this paper is to extract the amplitude-frequency characteristics of the sound generated by the transformer and combine with the corresponding detection algorithm to achieve the purpose of transformer fault detection by analyzing and extracting the amplitude-frequency characteristics of the sound produced by the transformer. The fault detection technology based on sound signal has been developed for decades, mainly used in nuclear power, aerospace and other advanced industries in the 1960s, in the seventies to ship, petrochemical, metallurgy and other industries; Since the 1980s, it has gradually expanded rapidly to various industries. During the inspection and maintenance of the transformer, the relevant staff member can rely on the sound heard in the operation of the transformer, and then know whether the transformer has broken down or not. In this paper, a transformer fault detection scheme simulating human auditory system is proposed according to the way that the maintenance personnel rely on sound to judge the transformer operation state. This scheme can be used as an effective auxiliary method for transformer fault detection. In this scheme, firstly, the sound emitted by the transformer is collected, then these sounds are judged by the detection system, and then the purpose of detecting the transformer operation is achieved. During the experiment, all kinds of sound emitted by the transformer were collected by the members of the experimental group, and the corresponding sound sample database was set up. Through the analysis, research, statistics and design of the corresponding algorithm, the fault detection system of substation power transformer is constructed. In this paper, a method of feature extraction based on the amplitude-frequency characteristics of transformer sound data is introduced, and each feature of the extracted sound data is composed of the corresponding one-dimensional vector or two-dimensional matrix. Then, principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA) algorithm should be used to reduce the dimension of the spectral features of the sound data, extract the main feature information, and then use the support vector machine (SVM) algorithm to classify the sound signals. In order to achieve the substation power transformer status detection purpose.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM63;TM41
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