本文選題：電力變壓器　切入點(diǎn)：故障診斷　出處：《華北電力大學(xué)》2017年碩士論文

【摘要】：根據(jù)電力變壓器油中溶解氣體組分中氣體成分和含量不同的特點(diǎn),通過監(jiān)測(cè)和檢測(cè)變壓器內(nèi)部氣體含量來診斷變壓器故障成為有效的手段之一。本文通過對(duì)基于油中溶解氣體分析(Dissolved Gas-in-oil Analysis,DGA)的各種變壓器診斷方法的優(yōu)缺點(diǎn)介紹,并在各類診斷方法進(jìn)行分析對(duì)比的基礎(chǔ)上,首次將具有較強(qiáng)特征提取能力的受限玻爾茲曼機(jī)(Restricted Boltzmann Machines,RBM)相關(guān)分類方法應(yīng)用于DGA變壓器故障診斷中,輔助檢修人員對(duì)其狀況進(jìn)行科學(xué)評(píng)估提供更為準(zhǔn)確的判斷。引入RBM學(xué)習(xí)算法基礎(chǔ)上,提出了基于分類受限玻爾茲曼機(jī)(Classification Restricted Boltzmann Machines,CRBM)的油浸式電力變壓器故障診斷方法。結(jié)合DGA數(shù)據(jù)特點(diǎn)以及變壓器故障類型,構(gòu)建了基于CRBM的變壓器故障診斷模型,并給出詳細(xì)的診斷步驟和實(shí)現(xiàn)過程。該方法具有較強(qiáng)的特征變換能力,其診斷結(jié)果以概率形式給出。提出了一種基于判別受限玻爾茲曼機(jī)(Discriminative Restricted Boltzmann Machines,DRBM)的油浸式電力變壓器故障診斷新方法。構(gòu)建了深度判別受限玻爾茲曼機(jī)(Deep Discriminative Restricted Boltzmann Machines,DDRBM)分類模型,通過數(shù)據(jù)集分類測(cè)試并與傳統(tǒng)神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)進(jìn)行對(duì)比后,應(yīng)用于變壓器故障診斷中,給出詳細(xì)的實(shí)現(xiàn)步驟。該方法具有較強(qiáng)的從大量樣本中提取數(shù)據(jù)特征能力,并且可以有效充分的利用變壓器檢測(cè)設(shè)備提取的無標(biāo)簽樣本進(jìn)行分類,以概率形式給出結(jié)果,有效判別故障類型。采用實(shí)例對(duì)所提出的兩種故障診斷方法進(jìn)行測(cè)試,并將兩種分類方法進(jìn)行對(duì)比分析,結(jié)果表明文中提出的兩種方法診斷性能較優(yōu),能夠更好滿足實(shí)際工程需要。
[Abstract]:According to the different components and contents of dissolved gases in power transformer oil, it is one of the effective methods to diagnose transformer faults by monitoring and detecting the gas content inside the transformer.In this paper, the advantages and disadvantages of various transformer diagnosis methods based on dissolved gas analysis in oil (dissolved Gas-in-oil Analysis) are introduced, and on the basis of analysis and comparison of various diagnostic methods,The restricted Boltzmann machines (RBM) classification method with strong feature extraction ability is applied to fault diagnosis of DGA transformers for the first time.Based on the RBM learning algorithm, a fault diagnosis method for oil-immersed power transformers based on classification Restricted Boltzmann machines is proposed.Combined with the characteristics of DGA data and the type of transformer fault, the transformer fault diagnosis model based on CRBM is constructed, and the detailed diagnosis steps and implementation process are given.This method has strong feature transformation ability, and its diagnosis results are given in the form of probability.A new fault diagnosis method for oil-immersed power transformers based on discriminative Restricted Boltzmann machines is proposed.A classification model of Deep Discriminative Restricted Boltzmann machines (DDRBM) for constrained Boltzmann machine with depth discrimination is constructed. The classification model is tested by data set and compared with traditional neural network and support vector machine, then it is applied to transformer fault diagnosis, and the implementation steps are given in detail.This method has a strong ability to extract data features from a large number of samples, and can effectively and fully use the untagged samples extracted by transformer detection equipment to classify. The results are given in the form of probability, and the fault types can be effectively identified.Two fault diagnosis methods proposed in this paper are tested by an example, and the two classification methods are compared and analyzed. The results show that the two methods proposed in this paper have better diagnostic performance and can better meet the actual engineering needs.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM41

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本文編號(hào)：1715871