本文選題：狀態(tài)評估 + 集對分析　； 參考：《上海電力學(xué)院》2014年碩士論文

【摘要】：在國家電網(wǎng)公司大力推廣“建設(shè)堅強智能電網(wǎng)”的今天，作為樞紐變電站的核心設(shè)備，電力變壓器直接影響著整個電網(wǎng)供電的安全和可靠。加之電力行業(yè)設(shè)備檢修制度的革新，狀態(tài)檢修技術(shù)不斷發(fā)展進步，作為實現(xiàn)狀態(tài)檢修的前提——狀態(tài)評估越來越受到研究學(xué)者的重視。目前，變壓器狀態(tài)評估方法大多仍局限于利用單一信息源信息分析判斷變壓器的健康狀態(tài)。但由于單一信息源的不確定性，狀態(tài)評估的準(zhǔn)確率往往會不盡如人意。因此，本文在建立變壓器健康狀態(tài)評估體系與合理選取狀態(tài)量的基礎(chǔ)上，研究了信息融合技術(shù)在變壓器健康狀態(tài)評估中的應(yīng)用，構(gòu)建并實現(xiàn)了具體模型，并針對狀態(tài)評估之后的故障診斷環(huán)節(jié)進行了深入研究，利用智能算法提高了故障診斷的準(zhǔn)確性，最終在前兩者的基礎(chǔ)上開發(fā)了基于虛擬儀器LabVIEW的變壓器健康狀態(tài)評估系統(tǒng)。 首先，通過研究變壓器故障類型與機理以及狀態(tài)評估系統(tǒng)的架構(gòu)方式，確定了變壓器各分部件狀態(tài)量的選取，，基于實際構(gòu)建了一套較為全面的變壓器分部件健康狀態(tài)評估體系。 其次，將改進層次分析法與熵權(quán)法以組合的方式優(yōu)化權(quán)重，并將引入集對分析方法分析變壓器健康狀態(tài)問題中，對其分析結(jié)果再利用D-S證據(jù)理論融合，建立起基于信息融合技術(shù)的變壓器健康狀態(tài)評估模型，并且用實例驗證，最終得到較為全面的變壓器分部件健康狀態(tài)評估結(jié)果。之后深入故障診斷環(huán)節(jié)研究，提出一種模擬退火算法與粒子群優(yōu)化算法相融合的改進算法，構(gòu)建自適應(yīng)概率神經(jīng)網(wǎng)絡(luò)故障診斷模型并以實例驗證。 最后，基于上述兩種模型，開發(fā)了基于LabVEW與MATLAB的混合編程的變壓器健康狀態(tài)評估系統(tǒng)平臺，該平臺實現(xiàn)了狀態(tài)評估、故障診斷以及數(shù)據(jù)庫管理三大模塊功能。
[Abstract]:As the core equipment of the hub substation, power transformer directly affects the security and reliability of the power supply of the whole power grid when the State Grid Company vigorously promotes "building a strong smart grid". In addition, with the innovation of equipment maintenance system in electric power industry and the continuous development and progress of condition-based maintenance technology, as a prerequisite to realize condition-condition maintenance, status assessment has been paid more and more attention by researchers. At present, most of the methods of transformer state evaluation are still limited to use single information source information analysis to judge the health status of transformer. However, due to the uncertainty of a single information source, the accuracy of state assessment is often unsatisfactory. Therefore, based on the establishment of transformer health assessment system and the reasonable selection of state quantity, the application of information fusion technology in transformer health state evaluation is studied, and the concrete model is constructed and realized. The fault diagnosis link after the state evaluation is deeply studied, and the accuracy of fault diagnosis is improved by using intelligent algorithm. Finally, a transformer health state evaluation system based on virtual instrument LabVIEW is developed. Firstly, by studying the type and mechanism of transformer faults and the structure of the state assessment system, the selection of the state quantities of each part of the transformer is determined. Based on the actual situation, a set of comprehensive assessment system for the health status of the transformer components is constructed. Secondly, the improved Analytic hierarchy process (AHP) and entropy weight method are combined to optimize the weight, and the set pair analysis method is introduced to analyze the health state of transformer. The results of the analysis are combined with D-S evidence theory. A transformer health state evaluation model based on information fusion technology is established and verified by an example. Finally, a more comprehensive assessment result of transformer health status is obtained. After further research on fault diagnosis, an improved algorithm combining simulated annealing algorithm and particle swarm optimization algorithm is proposed, and an adaptive probabilistic neural network fault diagnosis model is constructed and verified by an example. Finally, based on the above two models, a transformer health evaluation system platform based on LabVEW and MATLAB is developed. The platform realizes the functions of state assessment, fault diagnosis and database management.
【學(xué)位授予單位】：上海電力學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM41

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