本文選題：可視化 + 關(guān)聯(lián)規(guī)則挖掘��； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：隨著我國(guó)經(jīng)濟(jì)的蓬勃發(fā)展,用戶(hù)對(duì)電能質(zhì)量的要求也越來(lái)越高,配電網(wǎng)的安全運(yùn)行變得非常重要。配電網(wǎng)的可視化預(yù)警可以直觀(guān)、快速的顯示系統(tǒng)中的異常情況,將配電網(wǎng)的實(shí)時(shí)動(dòng)態(tài)反映給電網(wǎng)運(yùn)行和管理人員,防止異常狀態(tài)的惡化,保障智能配電網(wǎng)安全、穩(wěn)定、可靠的運(yùn)行。本文首先根據(jù)配電網(wǎng)SVG文件,對(duì)配電網(wǎng)網(wǎng)架結(jié)構(gòu)進(jìn)行解析與重構(gòu),之后以構(gòu)建了一種配電網(wǎng)預(yù)警評(píng)估的方法。本文將關(guān)聯(lián)規(guī)則挖掘方法引入配電網(wǎng)的關(guān)聯(lián)度評(píng)估中,根據(jù)配電網(wǎng)預(yù)警的需要對(duì)AprioriTid算法進(jìn)行優(yōu)化,應(yīng)用優(yōu)化后的算法得到元件的關(guān)聯(lián)度�？紤]到災(zāi)害氣候?qū)ε潆娋W(wǎng)元件的巨大影響,本文將災(zāi)害氣候數(shù)據(jù)與配電網(wǎng)運(yùn)行相關(guān)數(shù)據(jù)結(jié)合,利用相同方法進(jìn)行關(guān)聯(lián)規(guī)則挖掘,進(jìn)而計(jì)算故障概率。合成上述兩種參數(shù)和可靠性評(píng)估參數(shù),得到元件的預(yù)警評(píng)估結(jié)果。在預(yù)警評(píng)估的基礎(chǔ)上,使用基于信息熵和關(guān)聯(lián)規(guī)則挖掘的免疫算法和量子免疫算法兩種免疫算法得出預(yù)警結(jié)果。本文給出了免疫算法應(yīng)用在預(yù)警中的具體流程,并對(duì)算法進(jìn)行了分析。文章的最后以實(shí)際的配電網(wǎng)為例子,使用軟件驗(yàn)證了本文提出的預(yù)警方法。經(jīng)驗(yàn)證,本文提出的預(yù)警方法得出的預(yù)警結(jié)果與仿真結(jié)果大致一致,預(yù)警效果較好。本文所提出的配電網(wǎng)網(wǎng)架結(jié)構(gòu)解析與重構(gòu)方法和配電網(wǎng)預(yù)警方法已應(yīng)用于東北大學(xué)與國(guó)家電網(wǎng)遼寧省電力有限公司合作項(xiàng)目“提高配電網(wǎng)故障處理能力關(guān)鍵技術(shù)研究與開(kāi)發(fā)”的軟件中。
[Abstract]:With the rapid development of economy in China, the requirement of power quality is becoming higher and higher, and the safe operation of distribution network becomes very important. The visual early warning of distribution network can directly and quickly display the abnormal situation in the system, and reflect the real-time dynamic situation of the distribution network to the network operation and management personnel, prevent the deterioration of the abnormal state, and ensure the security and stability of the intelligent distribution network. Run reliably. In this paper, the distribution network structure is first analyzed and reconfigured according to the distribution network SVG file, and then a method of early warning evaluation of distribution network is constructed. In this paper, the association rules mining method is introduced into the evaluation of the distribution network correlation degree, and the AprioriTid algorithm is optimized according to the need of the distribution network early warning, and the correlation degree of the components is obtained by using the optimized algorithm. Considering the great influence of disaster climate on distribution network components, this paper combines disaster climate data with relevant data of distribution network operation, uses the same method to mine association rules, and then calculates the failure probability. The two parameters mentioned above and the reliability evaluation parameters are synthesized, and the early warning evaluation results of the components are obtained. On the basis of early warning evaluation, two immune algorithms based on information entropy and association rule mining and quantum immune algorithm are used to get the early warning results. In this paper, the flow chart of the application of immune algorithm in early warning is given, and the algorithm is analyzed. At the end of this paper, we use the software to verify the proposed early warning method, taking the actual distribution network as an example. It is verified that the early warning results obtained by the proposed method are approximately consistent with the simulation results, and the early warning effect is good. The analysis and reconfiguration method of distribution network structure and the early warning method of distribution network proposed in this paper have been applied to the cooperative project of Northeast University and State Grid Liaoning Electric Power Co., Ltd. "the key technology of improving the fault handling capacity of distribution network" Research and development of the software.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TM727;TP311.13

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相關(guān)期刊論文 前2條

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2 賴(lài)曉文;陳啟鑫;夏清;趙翔宇;楊明輝;張健;;基于SVG技術(shù)的電力系統(tǒng)可視化平臺(tái)集成與方法庫(kù)開(kāi)發(fā)[J];電力系統(tǒng)自動(dòng)化;2012年16期

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本文編號(hào)：1834988