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| 折扣與優(yōu)惠：團購最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要:現(xiàn)代設(shè)備技術(shù)水平不斷提高，生產(chǎn)率、自動化要求越來越高，相應地，故障也隨之增加。變壓器作為電力系統(tǒng)中非常復雜而且非常重要的設(shè)備，其工作狀態(tài)對電力系統(tǒng)、企事業(yè)單位生產(chǎn)及居民生活具有十分重要的影響。如何提前對變壓器故障進行預測和在故障發(fā)生后迅速判斷故障原因是提高工作效率、減少經(jīng)濟損失的一個重要途徑。因此研究變壓器故障診斷對保證系統(tǒng)安全、可靠、經(jīng)濟運行，提高經(jīng)濟效益具有重要意義。 概率神經(jīng)網(wǎng)絡(luò)（probabilistic neural networks）結(jié)構(gòu)簡單、訓練簡潔，利用概率神經(jīng)網(wǎng)絡(luò)模型的強大的非線性分類能力，將故障樣本空間映射到故障模式空間中，可形成一個具有較強容錯能力和結(jié)構(gòu)自適應能力的診斷網(wǎng)絡(luò)系統(tǒng)，從而提高故障診斷的準確率。本文在對油中溶解氣體分析法進行深入分析后，以改良三比值法為基礎(chǔ)，建立基于概率神經(jīng)網(wǎng)絡(luò)的故障診斷模型。然后，選取23組變壓器故障原始樣本數(shù)據(jù)對概率神經(jīng)網(wǎng)絡(luò)模型進行“學習”訓練，獲得了具有預測診斷功能的網(wǎng)絡(luò)模型；選取10組變壓器在線監(jiān)測數(shù)據(jù)作為測試數(shù)據(jù)，并查看了訓練數(shù)據(jù)網(wǎng)絡(luò)的分類效果圖和預測數(shù)據(jù)網(wǎng)絡(luò)的分類效果圖，結(jié)果只有兩個樣本判斷錯誤，即只有兩種變壓器的故障類型判斷錯誤，驗證了基于概率神經(jīng)網(wǎng)絡(luò)在變壓器故障預測診斷處理中的有效性。 關(guān)鍵詞 故障診斷 概率神經(jīng)網(wǎng)絡(luò) 變壓器 油中溶解氣體分析
Abstract:With the technical level of modern facility improves continually, the fault probability increases greatly. Power transformer has a very significant influence to power system, enterprise s production and people s life. How to forecast transformer s fault ahead and find the fault reason quickly after the fault is a good way to increase work efficiency and lighten the economy losing. Probabilistic neural network has the advantages of simple structure, simple training, the use of a probabilistic neural network model for strong nonlinear classification, fault sample space is mapped to a fault in the pattern space, can form a strong fault tolerant ability and structure of adaptive diagnosis system, so as to improve the accuracy of fault diagnosis. Based on the dissolved gas in oil analysis in-depth analysis, in order to improve the ratio of three as the basis, establish the fault diagnosis based on probabilistic neural network model. Then, select 23 group of transformer fault original sample data on the probabilistic neural network model of" learning" training, obtain the predictive diagnosis of functional network model; select 10 group of transformer on-line monitoring data as test data, and show the training data network classification effect diagram and the predicted data network classification effect chart, only the results of a sample of two errors of judgment, that only two transformer fault type judgement error, verification based on probabilistic neural network in transformer fault forecast and diagnosis treatment effectiveness. Keywords fault diagnosis, probability neural networks（PNN）,power transformer,Dissolved Oas Analysis（DGA）
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