本文選題：BP神經(jīng)網(wǎng)絡(luò) + 故障預(yù)測��； 參考：《廣東工業(yè)大學(xué)》2017年碩士論文

【摘要】：伴隨我國社會經(jīng)濟和科技的高速發(fā)展,電力需求量在大幅度增長,這樣就促使了電力缺口越來越大,人們也對電網(wǎng)企業(yè)供電的穩(wěn)定性與可靠性有了更高標(biāo)準(zhǔn)的要求,如此就推動了電網(wǎng)規(guī)模擴大、電力設(shè)備量急劇增長的進程。電網(wǎng)規(guī)模的擴大和設(shè)備的增多,能更好地滿足人們的生產(chǎn)生活需求,但隨之而來的是故障增多,停電范圍擴大和停電時間增加等嚴(yán)重影響生產(chǎn)生活的問題。所以,保證電網(wǎng)正常、可靠運行,避免設(shè)備故障或少發(fā)生故障,且能在故障后能夠迅速、準(zhǔn)確地定位并排除,這對于運行維護人員是個巨大的挑戰(zhàn)。為彌補傳統(tǒng)維修方式的不足,人們借助計算機技術(shù)、狀態(tài)監(jiān)測和故障診斷技術(shù)等新銳技術(shù),創(chuàng)造出了新的維修方式,就是基于狀態(tài)的維修(Condition Based Maintenance,CBM),也稱為視情維修。這一維修方式充分運用各種技術(shù)手段來獲取設(shè)備運行時的數(shù)據(jù),再利用故障預(yù)測和診斷技術(shù)進行綜合分析,確定設(shè)備運行狀態(tài),然后預(yù)測其發(fā)展趨勢以及會發(fā)生何種故障、何時發(fā)生和何地發(fā)生,實現(xiàn)能通過在線監(jiān)測設(shè)備狀態(tài)、預(yù)測即將發(fā)生的故障和制訂合理的預(yù)防措施或維修策略的重大目標(biāo)�；跔顟B(tài)的維修主要是能根據(jù)每個設(shè)備不同的運行狀態(tài)來預(yù)測其劣化程度及趨勢,并對設(shè)備檢修做出合理、科學(xué)的維修決策,判斷是否有需要對設(shè)備進行預(yù)防性維修和維修何時進行,將故障抑制在萌芽狀態(tài),所以,其維修間隔期并不固定。這對于電網(wǎng)企業(yè)而言,能提高企業(yè)的供電可靠性,解決電能質(zhì)量低、故障停電時間多、故障停電范圍大和配網(wǎng)系統(tǒng)運行的經(jīng)濟性低等問題,降低運行維護費用,提升設(shè)備維護和維修水平,實現(xiàn)精確維修,提高企業(yè)經(jīng)濟效益,提升企業(yè)對電網(wǎng)的管理水平和工作效率,提升企業(yè)“為人民服務(wù)”的形象,對公眾的承諾得到兌現(xiàn)。而故障預(yù)測是故障診斷的重要組成部分,它通過分析歷史和當(dāng)前數(shù)據(jù),篩選提取出設(shè)備故障特征值及其運行發(fā)展趨勢,進而對設(shè)備未來的運行狀態(tài)和可能出現(xiàn)的故障進行預(yù)測,確定設(shè)備運行狀態(tài)級別,提早掌握設(shè)備劣化趨勢,做到提早預(yù)防和修復(fù)。利用故障預(yù)測來解決設(shè)備故障問題,這樣不只具有重要的理論探索價值,而且還具有廣泛的工程應(yīng)用意義。本論文結(jié)合基于狀態(tài)的維修技術(shù)和神經(jīng)網(wǎng)絡(luò)技術(shù),提出基于在線運行設(shè)備故障預(yù)測的模型。該模型根據(jù)故障的嚴(yán)重性,將風(fēng)險等級劃分為四個級別,分別以“Ⅰ級、Ⅱ級、Ⅲ級和Ⅳ級”來表示,這既能看出設(shè)備未來的運行狀態(tài),也有助于差異化維修的決策。通過分析處理歷史數(shù)據(jù),對故障特征值進行提取及收集,形成特征值樣本集,再利用樣本集來訓(xùn)練設(shè)計好的神經(jīng)網(wǎng)絡(luò),調(diào)整權(quán)重,對神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)設(shè)計進行優(yōu)化,建立基于神經(jīng)網(wǎng)絡(luò)的故障預(yù)測模型,以達到對設(shè)備故障的預(yù)測的目的。
[Abstract]:With the rapid development of social economy and science and technology in our country, the demand for electric power is increasing by a large margin, which makes the gap of electric power more and more large, and people also have higher requirements for the stability and reliability of power supply in power grid enterprises. In this way, the expansion of the scale of the power grid, the rapid growth of power equipment process. The expansion of power grid and the increase of equipment can better meet the needs of people's production and life, but with the increase of failures, the range of power outages and the time of power outages will seriously affect the production and life. Therefore, it is a great challenge for the operation and maintenance personnel to ensure the normal and reliable operation of the power network, to avoid the equipment fault or to avoid the fault, and to be able to locate and eliminate it quickly and accurately after the failure. In order to make up for the deficiency of the traditional maintenance method, people have created a new maintenance method, which is condition based maintenance (CBM), also called condition based maintenance (CBM), with the help of computer technology, condition monitoring and fault diagnosis technology and so on. This maintenance method makes full use of all kinds of technical means to obtain the data while the equipment is running, and then uses the fault prediction and diagnosis technology to carry on the comprehensive analysis, determines the equipment running status, then predicts its development trend and what kind of malfunction will occur. When and where will happen, realize the important goal that can monitor the status of the equipment online, predict the upcoming failure and make reasonable preventive measures or maintenance strategy. Condition-based maintenance can predict the deterioration degree and trend of each equipment according to its different operating state, and make reasonable and scientific maintenance decision for equipment maintenance. Whether it is necessary to carry out preventive maintenance and when to carry out maintenance, the fault will be restrained in the embryonic state, therefore, the maintenance interval is not fixed. For power grid enterprises, it can improve the reliability of power supply, solve the problems of low power quality, more time of failure, large range of failure and low economy of distribution network operation, and reduce the cost of operation and maintenance. Improve the level of equipment maintenance and maintenance, achieve accurate maintenance, improve the economic efficiency of enterprises, improve the management level and working efficiency of enterprises to the power grid, enhance the image of enterprises "serving the people", and fulfill the promise to the public. Fault prediction is an important part of fault diagnosis. By analyzing the history and current data, it extracts the characteristic value of equipment fault and its development trend, and then predicts the future running state and possible faults of the equipment. Determine equipment operation status level, grasp equipment deterioration trend early, achieve early prevention and repair. Using fault prediction to solve the problem of equipment failure not only has important theoretical exploration value, but also has a wide range of engineering application significance. In this paper, a fault prediction model based on on-line operation equipment is proposed by combining state based maintenance technology and neural network technology. According to the severity of the fault, the model divides the risk level into four levels, which are expressed as "鈪，

本文編號：2073951