本文選題：電力變壓器 + 在線監(jiān)測　； 參考：《哈爾濱理工大學(xué)》2017年碩士論文

【摘要】：在電網(wǎng)中,電力變壓器數(shù)量眾多且安裝位置分散,存在維護(hù)成本高、不利于集中監(jiān)測等問題,經(jīng)常出現(xiàn)變壓器的運(yùn)行數(shù)據(jù)及運(yùn)行狀態(tài)無法實(shí)時(shí)監(jiān)測的問題,運(yùn)行過程中出現(xiàn)的不正�，F(xiàn)象難以及時(shí)發(fā)現(xiàn),給用戶帶來很大的不便,不符合國家提出的節(jié)能增效目標(biāo)和國家電網(wǎng)公司精細(xì)化管理的要求。本文綜合比較分析了目前主要變壓器監(jiān)測設(shè)備所側(cè)重的監(jiān)測參數(shù)及功能,在此基礎(chǔ)上提出了一種基于多參數(shù)融合的智能變壓器監(jiān)測及診斷預(yù)警系統(tǒng)。分析了變壓器智能監(jiān)測預(yù)警的技術(shù)要求,根據(jù)運(yùn)行監(jiān)測的不同參數(shù)、安裝位置及傳感器類型,提出并設(shè)計(jì)了一主三從的智能在線監(jiān)測系統(tǒng)方案;同時(shí),對變壓器的故障類型和先兆特征進(jìn)行了分類研究,得到常見多發(fā)故障的先兆特征參數(shù),根據(jù)直接獲得的基本監(jiān)測數(shù)據(jù)進(jìn)行了能夠反映故障先兆的衍生數(shù)據(jù)計(jì)算,確定了故障預(yù)警分析的特征參量,從而建立了一種新型的變壓器智能監(jiān)測預(yù)警系統(tǒng)平臺,進(jìn)行了相應(yīng)的軟硬件設(shè)計(jì)。隨后,在分析比較了幾種預(yù)測算法的基礎(chǔ)上,結(jié)合變壓器智能監(jiān)測預(yù)警的具體特點(diǎn),采用BP神經(jīng)網(wǎng)絡(luò)建立了變壓器智能在線監(jiān)測預(yù)警系統(tǒng)的數(shù)學(xué)模型,針對上述設(shè)計(jì)的系統(tǒng)平臺采集到的變壓器連續(xù)運(yùn)行數(shù)據(jù),選取了五個(gè)特征參數(shù)進(jìn)行BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練,對該組特征參數(shù)所能夠反映的變壓器故障進(jìn)行了預(yù)測,并與實(shí)測故障先兆數(shù)據(jù)對比,仿真計(jì)算結(jié)果表明:本文提出的采用BP神經(jīng)網(wǎng)絡(luò)針對特定故障先兆特征參數(shù)的預(yù)測方法與實(shí)測故障數(shù)據(jù)基本吻合,從而證明該系統(tǒng)能夠?qū)崿F(xiàn)變壓器智能在線故障監(jiān)測及預(yù)警。通過本文所設(shè)計(jì)的智能監(jiān)測預(yù)警系統(tǒng),不僅能夠?qū)崿F(xiàn)變壓器運(yùn)行過程中幾乎全部狀態(tài)參數(shù)的長期連續(xù)測量,而且能夠依據(jù)這些數(shù)據(jù)自動進(jìn)行變壓器故障先兆特征的查找、判斷,并自動發(fā)出預(yù)警信號;同時(shí)還能夠建立變壓器運(yùn)行參數(shù)大數(shù)據(jù)庫,便于進(jìn)一步分析變壓器故障產(chǎn)生的原因。
[Abstract]:In the power network, the number of power transformers is numerous and the installation position is scattered, there are many problems such as high maintenance cost, which is not conducive to centralized monitoring, and the problems of transformer operation data and operation state can not be monitored in real time. The abnormal phenomenon in the operation process is difficult to find in time, which brings great inconvenience to the users, and does not accord with the goal of energy saving and increasing efficiency put forward by the state and the fine management requirements of the State Grid Company. In this paper, the monitoring parameters and functions of the main transformer monitoring equipments are compared and analyzed, and an intelligent transformer monitoring and diagnosis early warning system based on multi-parameter fusion is proposed. The technical requirements of transformer intelligent monitoring and warning are analyzed. According to the different parameters of operation monitoring, installation position and sensor type, an intelligent on-line monitoring system with one main and three followings is put forward and designed, at the same time, The fault types and precursory characteristics of transformers are classified, and the precursor characteristic parameters of common multiple faults are obtained. The derived data which can reflect the fault precursors are calculated according to the basic monitoring data obtained directly. The characteristic parameters of fault early warning analysis are determined, and a new transformer intelligent monitoring and warning system platform is established, and the corresponding software and hardware are designed. Then, based on the analysis and comparison of several prediction algorithms, combined with the specific characteristics of transformer intelligent monitoring and early warning, the mathematical model of transformer intelligent on-line monitoring and warning system is established by using BP neural network. In view of the continuous operation data of transformer collected from the system platform, five characteristic parameters are selected for BP neural network training, and the transformer faults which can be reflected by this set of characteristic parameters are predicted. Compared with the measured fault precursor data, the simulation results show that the BP neural network method proposed in this paper is in good agreement with the measured fault data. It is proved that the system can realize intelligent on-line fault monitoring and early warning of transformer. Through the intelligent monitoring and warning system designed in this paper, it can not only realize the long-term continuous measurement of almost all the state parameters in the process of transformer operation, but also automatically search and judge the precursory features of transformer faults according to these data. At the same time, the large database of transformer operation parameters can be set up, which is convenient for further analysis of the causes of transformer faults.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM41

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