本文關(guān)鍵詞：智能變電站狀態(tài)監(jiān)測系統(tǒng)的設(shè)計研究及應(yīng)用　出處：《華東理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 智能變電站 狀態(tài)監(jiān)測 粗糙集理論 變壓器 故障診斷

【摘要】：智能變電站是實現(xiàn)電能集中分配與電壓電流變換的場所,在輸配電系統(tǒng)中具有極其重要的地位。因此當(dāng)變電站發(fā)生故障時,需要相關(guān)運行人員快速準(zhǔn)確地定位故障區(qū)域,識別真正的故障元件并將其隔離,從而快速恢復(fù)非故障區(qū)域的正常運行。但是,由于系統(tǒng)發(fā)生復(fù)雜的多重故障、開關(guān)或保護(hù)在上傳信息時存在信道干擾造成的信息丟失等因素的影響,其后臺報警信息往往存在不確定性和不完整性的問題,給狀態(tài)監(jiān)測和故障診斷工作造成很多困難。本文在綜合分析數(shù)據(jù)挖掘算法的基本原理的基礎(chǔ)上,結(jié)合智能變電站的數(shù)據(jù)類型、網(wǎng)絡(luò)結(jié)構(gòu)和過程層的配置,并根據(jù)數(shù)據(jù)采集的特征和智能變電站狀態(tài)監(jiān)測信息的特點以及故障診斷信息,設(shè)計了數(shù)據(jù)挖掘算法在智能變電站中系統(tǒng)架構(gòu),仿真了的數(shù)據(jù)挖掘算法在智能變電站中的應(yīng)用的可行性。并以實際的110kV變電站為例,仿真了數(shù)據(jù)挖掘方法對智能變電站的整體與局部采取不同狀態(tài)監(jiān)測方法的影響。利用基于遺傳算法的粗糙集方法對110kV區(qū)域為整體進(jìn)行故障診斷仿真實驗。結(jié)果表明該方法確是一種快速準(zhǔn)確、容錯性強(qiáng)、適應(yīng)性好的智能變電站狀態(tài)監(jiān)測策略方法,對實現(xiàn)高效的在線智能變電站狀態(tài)監(jiān)測具有重要的意義。以變壓器為局部對象研究數(shù)據(jù)挖掘技術(shù)在變壓器狀態(tài)監(jiān)測中的應(yīng)用。實驗表明基于粒子群的多核支持向量機(jī)算法可以充分保證計算速度和較高故障判斷精度,并且該模型能確定變壓器故障種類,并具有較高的正確率。
[Abstract]:Intelligent substation is the place to realize the centralized distribution of electric energy and the conversion of voltage and current, which plays an extremely important role in the transmission and distribution system. It is necessary for the relevant operators to locate the fault area quickly and accurately, identify and isolate the real fault elements, so as to quickly restore the normal operation of the non-fault area. However, complex multiple faults occur in the system. The information loss caused by channel interference exists in the switching or protection of uploading information, and the background alarm information often has the problem of uncertainty and incompleteness. On the basis of comprehensive analysis of the basic principles of data mining algorithm, combined with the data types of intelligent substation, network structure and process layer configuration. According to the characteristics of data acquisition, the characteristics of intelligent substation state monitoring information and fault diagnosis information, the system architecture of data mining algorithm in intelligent substation is designed. The feasibility of the application of the simulated data mining algorithm in the intelligent substation is presented, and the actual 110 kV substation is taken as an example. The influence of data mining method on the whole and local state monitoring methods of intelligent substation is simulated. The fault diagnosis simulation experiment of 110kV region is carried out using rough set method based on genetic algorithm. The results show that the method is fast and accurate. An intelligent substation condition monitoring strategy with strong fault tolerance and good adaptability. The application of data mining technology in transformer condition monitoring based on PSO is studied. The experiment shows that the multi-core support direction based on particle swarm optimization (PSO) is very important to the realization of on-line intelligent substation state monitoring. The calculation speed and the accuracy of fault determination can be fully guaranteed by the metering algorithm. And the model can determine the type of transformer fault, and has a high accuracy.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM76;TM63;TP274

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