本文選題：電氣火災(zāi) + 故障電弧　； 參考：《燕山大學(xué)》2013年博士論文

【摘要】：火推動了人類社會的文明進步，而火災(zāi)卻給人類帶來了巨大的危害。隨著現(xiàn)代社會經(jīng)濟的快速發(fā)展以及工業(yè)的不斷繁榮，各種電氣化產(chǎn)品的層出不窮，給火災(zāi)的發(fā)生提供了更大的可能性。多年來電氣火災(zāi)的數(shù)量一直呈現(xiàn)居高不下的局面，而且損失慘重的重特大火災(zāi)往往也由電氣火災(zāi)造成。傳統(tǒng)的火災(zāi)預(yù)報由于探測技術(shù)、信號處理方法和理論研究的局限性，在電氣火災(zāi)監(jiān)測過程中時常會出現(xiàn)誤報及漏報。 論文深入分析了電氣火災(zāi)形成機理，在分析出電�。娀鸹ǎ┖透邷貫殡姎饣馂�(zāi)火源的根本形式的基礎(chǔ)上，通過大量實驗，深入研究了不同負載形式下的交流故障電弧燃燒時的電弧電壓、電流波形特性后發(fā)現(xiàn)，交流電弧在燃燒過程中有潛在著的“零休現(xiàn)象”。故障電弧的“零休現(xiàn)象”特性，給故障電弧的檢測拓寬了思路。提出了利用故障電弧檢測與分析監(jiān)測及預(yù)報電氣火災(zāi)的方法。 運用小波函數(shù)對故障電弧電流信號進行了小波奇異性分析。構(gòu)造了正交二次樣條小波為小波函數(shù)，利用多孔算法的二進小波變換實現(xiàn)了快速小波變換算法。故障電弧周期零休現(xiàn)象這一特征信息用小波分析時表現(xiàn)為周期性的奇異點，因此提出了周期性奇異點檢測故障電弧的新算法，并分析了該故障電弧檢測算法的可行性和有效性。 在檢測故障電弧發(fā)生的基礎(chǔ)上，對電氣火災(zāi)早期現(xiàn)場的主要特征信號進行了多參數(shù)實時監(jiān)測，運用多信息融合技術(shù)完成了對所探測的電氣火災(zāi)特征信息的融合，實現(xiàn)了電氣火災(zāi)的準確辨識。設(shè)計了基于故障電弧的信息融合的三層模型，并運用我國標準火數(shù)據(jù)以及典型干擾數(shù)據(jù)進行了實驗仿真，仿真結(jié)果表明，該融合模型能夠很好地完成電氣火災(zāi)的快速準確預(yù)報，有效地避免了電氣火災(zāi)的誤報和漏報。 采用集散控制方法，完成了基于故障電弧和多信息融合的電氣火災(zāi)預(yù)報系統(tǒng)的系統(tǒng)設(shè)計。整個系統(tǒng)分為上下位機，下位機又分為主機和從機。下位機主要完成信號的采集、預(yù)處理以及傳輸，其中的主機可完成一定的信號處理與判斷；上位機主要完成各種信號處理算法的實現(xiàn)、存儲以及監(jiān)控系統(tǒng)畫面的實現(xiàn)。所研發(fā)的“電氣火災(zāi)預(yù)報系統(tǒng)”經(jīng)過了反復(fù)試驗、調(diào)試并在多家應(yīng)用單位進行了推廣使用，較好地實現(xiàn)了電氣火災(zāi)的預(yù)防。 論文在電氣火災(zāi)預(yù)報方面進行了一定的研究工作，取得了一定的進展，但是，電氣火災(zāi)仍然有許多值得研究的熱點，例如，在故障電弧進一步與電氣火災(zāi)其他參量的融合方面、電氣火災(zāi)融合模型結(jié)構(gòu)的優(yōu)化方面、采用新型探測技術(shù)和探測器擴展現(xiàn)有系統(tǒng)的能力方面、其它領(lǐng)域的新技術(shù)（如激光圖像粒徑分群、激光前向/后向散射的應(yīng)用）引發(fā)電氣火災(zāi)探測技術(shù)的新途徑方面以及電氣火災(zāi)監(jiān)測技術(shù)在與自動化、現(xiàn)代通訊技術(shù)、智能大廈技術(shù)的進一步結(jié)合使得電氣火災(zāi)探測系統(tǒng)更趨于自動化、開放性和模塊化等方面還會有更進一步的發(fā)展。
[Abstract]:Fire has promoted the civilization and progress of human society, and fire has brought great harm to mankind. With the rapid development of modern social economy and the continuous prosperity of industry, the emergence of various electrified products has provided greater possibility for the occurrence of fire. The number of caller gas fires has been in high level for many years. And the heavy and heavy fires are often caused by electrical fires. The traditional fire prediction, due to the limitations of detection techniques, signal processing methods and theoretical research, often Misreports and Misreports in the process of electrical fire monitoring.
In this paper, the formation mechanism of electric fire is deeply analyzed. On the basis of the basic form of arc (electric spark) and high temperature as the fire source of electric fire, through a lot of experiments, the arc voltage of alternating current fault arc burning under different load forms is deeply studied. After the current wave shape characteristics, it is found that the AC arc has the potential in the combustion process. The "zero rest" phenomenon, the "zero rest" characteristic of the fault arc, has widened the idea of the detection of the fault arc. The method of using the fault arc detection and analysis to monitor and predict the electric fire is put forward.
Wavelet function is used to analyze the singularity of the fault arc current signal by using the wavelet function. The orthogonal two spline wavelet is used as the wavelet function, and the fast wavelet transform algorithm is realized by the two progressive wavelet transform of the porous algorithm. The characteristic information of the fault arc period zero rest is a periodic singular point in the small wave analysis. This paper proposes a new algorithm for detecting arc fault by cyclic singularity detection, and analyzes the feasibility and effectiveness of the algorithm.
On the basis of detecting the occurrence of the fault arc, the main characteristic signals in the early stage of the electric fire are monitored in real time, and the information fusion of the electric fire detection is completed by the multi information fusion technology, and the accurate identification of the electric fire is realized. The three layer model of information fusion based on the fault arc is designed. The experimental simulation is carried out with the standard fire data and typical interference data in China. The simulation results show that the fusion model can complete the rapid and accurate prediction of electrical fire, and effectively avoid the false alarm and false alarm of electrical fire.
The system design of electric fire prediction system based on the fault arc and multi information fusion is completed by the method of distributed control. The whole system is divided into the upper and lower computer, the lower machine is divided into the host and the slave. The lower machine mainly completes the acquisition, preprocessing and transmission of the signal, and the host can complete certain signal processing and judgment; The bit machine mainly completes the realization of various signal processing algorithms, stores and monitors the realization of the system picture. The "electric fire prediction system" has been tested repeatedly, debugged and popularized in many application units, and the electric fire prevention is well realized.
In this paper, some research work has been carried out in the field of electric fire prediction, and some progress has been made. However, there are still a lot of hot spots in the electric fire. For example, in the aspect of the fusion of the fault arc and other parameters of the electric fire, the optimization of the structure of the electric fire fusion model, the new detection technology and detection are adopted. The ability to expand existing systems, new technologies in other fields (such as laser image particle size distribution, application of laser forward / backward scattering) lead to a new approach to electrical fire detection technology and electrical fire monitoring technology in conjunction with automation, modern communication technology, Intelligent Building technology to make electrical fire detection The system will be more automated, and there will be further development in terms of openness and modularity.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TM76;X934

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