栓接电缆连接器松动故障诊断的研究

发布时间：2018-06-25 03:45

本文选题：电连接 + 电火花故障　；参考：《辽宁工程技术大学》2015年硕士论文

【摘要】：随着国内的经济快速的发展、城市化的建设也日益加快,使用电缆的范围也逐渐增多,布局也变得更加复杂,使得电缆出现故障的频率逐渐增加、种类逐渐增多。其中最容易产生的故障是由于电缆栓接松动产生的电火花故障,如不及时控制,将发展为故障电弧,进而导致经济损失严重,甚至威胁到人身安全。因此,快速、准确的检测电缆螺栓连接松动产生的电火花故障并及时采取相应措施具有重大的意义。本文开展了电缆栓接松动产生电火花故障的研究工作,研究电缆栓接松动产生的电火花特性,由于电火花是产生故障电弧的前一阶段,可以将电火花看作瞬时小电弧,因此本文也介绍了电弧的特性。同时,为了研究电火花故障检测的方法,本文研制了电缆栓接松动的电火花发生装置,并在不同回路电流、不同回路功率因数以及螺栓不同松动程度的条件下,分别进行了大量的实验,同时通过数据采集卡收集大量的实验数据,并创建相关的数据库,为下一步对数据的分析奠定了基础。为找出不同实验条件下发生电火花故障与正常状态时电流的特性以及它们相互之间的区别,本文利用小波包能量熵和概率神经网络相结合的方法,对接触电流进行研究并对螺栓松动产生的电火花故障进行判别。分析结果表明,采用小波包能量熵算法能够有效提取电缆栓接松动电火花故障时刻电流的特征,把该特征作为概率神经网络(PNN)的输入,可以有效识别配电网络或者线路中是否发生了电缆松动电火花故障。
[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.
【学位授予单位】：辽宁工程技术大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM503.5

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