本文選題：電連接 + 電火花故障　； 參考：《遼寧工程技術(shù)大學(xué)》2015年碩士論文

【摘要】：隨著國內(nèi)的經(jīng)濟(jì)快速的發(fā)展、城市化的建設(shè)也日益加快,使用電纜的范圍也逐漸增多,布局也變得更加復(fù)雜,使得電纜出現(xiàn)故障的頻率逐漸增加、種類逐漸增多。其中最容易產(chǎn)生的故障是由于電纜栓接松動產(chǎn)生的電火花故障,如不及時控制,將發(fā)展為故障電弧,進(jìn)而導(dǎo)致經(jīng)濟(jì)損失嚴(yán)重,甚至威脅到人身安全。因此,快速、準(zhǔn)確的檢測電纜螺栓連接松動產(chǎn)生的電火花故障并及時采取相應(yīng)措施具有重大的意義。本文開展了電纜栓接松動產(chǎn)生電火花故障的研究工作,研究電纜栓接松動產(chǎn)生的電火花特性,由于電火花是產(chǎn)生故障電弧的前一階段,可以將電火花看作瞬時小電弧,因此本文也介紹了電弧的特性。同時,為了研究電火花故障檢測的方法,本文研制了電纜栓接松動的電火花發(fā)生裝置,并在不同回路電流、不同回路功率因數(shù)以及螺栓不同松動程度的條件下,分別進(jìn)行了大量的實(shí)驗(yàn),同時通過數(shù)據(jù)采集卡收集大量的實(shí)驗(yàn)數(shù)據(jù),并創(chuàng)建相關(guān)的數(shù)據(jù)庫,為下一步對數(shù)據(jù)的分析奠定了基礎(chǔ)。為找出不同實(shí)驗(yàn)條件下發(fā)生電火花故障與正常狀態(tài)時電流的特性以及它們相互之間的區(qū)別,本文利用小波包能量熵和概率神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法,對接觸電流進(jìn)行研究并對螺栓松動產(chǎn)生的電火花故障進(jìn)行判別。分析結(jié)果表明,采用小波包能量熵算法能夠有效提取電纜栓接松動電火花故障時刻電流的特征,把該特征作為概率神經(jīng)網(wǎng)絡(luò)(PNN)的輸入,可以有效識別配電網(wǎng)絡(luò)或者線路中是否發(fā)生了電纜松動電火花故障。
[Abstract]:With the rapid development of domestic economy, the construction of urbanization is accelerating day by day, the range of using cable is increasing gradually, and the layout is becoming more complex, which makes the frequency of cable fault increase gradually, and the kinds of cable increase gradually. The most easily produced fault is the electrical spark fault caused by the loosening of the cable bolt. If it is not controlled in time, it will develop into a fault arc, which will lead to serious economic losses and even threaten the personal safety. Therefore, it is of great significance to quickly and accurately detect the EDM fault caused by the loosening of cable bolt connection and take corresponding measures in time. In this paper, the research work of electric spark fault caused by cable bolting loosening is carried out, and the characteristics of electric spark caused by cable bolting loosening are studied. Because the electric spark is the previous stage of the fault arc, the electric spark can be regarded as a transient small arc. Therefore, this paper also introduces the characteristics of the arc. At the same time, in order to study the method of EDM fault detection, this paper developed the EDM generator of cable bolt loosening, and under the conditions of different circuit current, different circuit power factor and different degree of bolt loosening, A large number of experiments were carried out, and a large number of experimental data were collected through the data acquisition card, and related databases were created, which laid a foundation for the analysis of the data in the next step. In order to find out the characteristics of electric discharge current in different experimental conditions and the differences between them, the wavelet packet energy entropy and probabilistic neural network are combined in this paper. The contact current is studied and the EDM fault caused by bolt loosening is identified. The analysis results show that the wavelet packet energy entropy algorithm can effectively extract the characteristic of the current at the fault time of the loose electric spark in the cable bolting, and use this feature as the input of the probabilistic neural network (PNN). Can effectively identify the distribution network or whether cable loose EDM fault occurred in the line.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM503.5

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