本文關(guān)鍵詞：基于PAS與DFNN的變壓器故障預(yù)測(cè)研究　出處：《河北聯(lián)合大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： PAS D-FNN 電力變壓器 故障預(yù)測(cè) MATLAB

【摘要】：變壓器是電力系統(tǒng)和智能變電站中重要的電力設(shè)備，智能變壓器智能化水平關(guān)系著智能變電站運(yùn)行的可靠性和投資的經(jīng)濟(jì)性。而變壓器故障預(yù)測(cè)能夠發(fā)現(xiàn)潛伏的故障以及預(yù)告故障的發(fā)展趨勢(shì)，研究故障預(yù)測(cè)對(duì)系統(tǒng)安全運(yùn)行和變壓器的狀態(tài)檢修有重要意義。 動(dòng)態(tài)模糊神經(jīng)網(wǎng)絡(luò)具有強(qiáng)大的多元非線性數(shù)據(jù)處理和函數(shù)逼近功能，能夠利用原始樣本數(shù)據(jù)通過(guò)模型內(nèi)部自我學(xué)習(xí)訓(xùn)練獲得準(zhǔn)確度較高的預(yù)測(cè)診斷模型。將動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)強(qiáng)大的預(yù)測(cè)診斷功能引入到變壓器故障處理中，建立起能夠真實(shí)反映變壓器故障特性的智能預(yù)測(cè)診斷模型，能夠?qū)崿F(xiàn)變壓器故障在線檢測(cè)的要求，提高變電站的綜合自動(dòng)化水平。本課題結(jié)合變壓器故障預(yù)測(cè)診斷在線監(jiān)測(cè)的特點(diǎn)，選用了光聲光譜技術(shù)對(duì)變壓器油中的故障氣體的含量進(jìn)行實(shí)時(shí)在線的監(jiān)測(cè)，，選取了動(dòng)態(tài)模糊神經(jīng)網(wǎng)絡(luò)為實(shí)驗(yàn)的主要模型結(jié)構(gòu)，利用MATLAB中的神經(jīng)網(wǎng)絡(luò)工具箱，建立起基于動(dòng)態(tài)模糊神經(jīng)網(wǎng)絡(luò)的電力變壓器故障預(yù)測(cè)模型。 實(shí)驗(yàn)選取了150組變壓器故障原始樣本數(shù)據(jù)對(duì)D-FNN模型中進(jìn)行學(xué)習(xí)訓(xùn)練，得到了具有預(yù)測(cè)診斷功能的網(wǎng)絡(luò)模型；再挑選100組變壓器的在線監(jiān)測(cè)數(shù)據(jù)進(jìn)行仿真試驗(yàn)，并查查看了模型預(yù)算誤差收斂曲線，證明了采用基于PAS與DFNN變壓器故障診斷預(yù)測(cè)模型預(yù)測(cè)變壓器故障相對(duì)于傳統(tǒng)的方法具有更高的故障診斷率,驗(yàn)證了基于PAS與DFNN在變壓器故障預(yù)測(cè)診斷處理中的合理有效性。
[Abstract]:Transformer is an important power equipment in power system and intelligent substation. Intelligent transformer intelligence level relates to the reliability of intelligent substation operation and the economy of investment, and transformer fault prediction can find latent faults and forecast the development trend of fault. It is important to study the fault prediction for the safe operation of the system and the condition maintenance of the transformer. Dynamic fuzzy neural network has powerful functions of multivariate nonlinear data processing and function approximation. The predictive diagnosis model with high accuracy can be obtained by using the original sample data through self-learning training within the model. The powerful predictive diagnosis function of dynamic neural network is introduced into transformer fault processing. An intelligent predictive diagnosis model which can truly reflect the fault characteristics of transformers is established and the requirement of on-line detection of transformer faults can be realized. Based on the characteristics of on-line monitoring of transformer fault prediction and diagnosis, the photoacoustic spectrum technology is used to monitor the content of fault gas in transformer oil in real time and online. The dynamic fuzzy neural network is selected as the main model structure of the experiment, and the power transformer fault prediction model based on the dynamic fuzzy neural network is established by using the neural network toolbox in MATLAB. 150 sets of original transformer fault samples are selected for learning and training in D-FNN model, and a network model with predictive diagnosis function is obtained. Then the on-line monitoring data of 100 groups of transformers are selected for simulation test, and the convergence curve of model budget error is checked. It is proved that the prediction model of transformer fault based on PAS and DFNN has higher fault diagnosis rate than the traditional method. The validity of PAS and DFNN in transformer fault diagnosis and treatment is verified.
【學(xué)位授予單位】：河北聯(lián)合大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM407

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