【作者】 李林；

【导师】 冯勇；

【作者基本信息】 哈尔滨工业大学 ， 电气工程， 2007， 硕士

【摘要】 随着配电网中性点经小电阻接地的方式在我国推广,高阻抗故障的检测引起了人们的重视。由于高阻抗故障引起的故障电流较小,不能使过流保护装置产生动作,因此它的检测比较困难。在故障发生后若不及时采取有效措施,会对人的生命安全造成威胁,并有引发火灾的可能。本课题来源于重庆大学教育部重点实验室访问学者项目,对小电阻接地配电网发生高阻抗故障时的特征及检测方法进行了研究。本文首先利用对称分量法推导了配电网发生单相接地故障时的相电压、故障点对地电流以及中性点电压与过渡电阻之间的数学表达式,比较了中性点经小电阻接地和消弧线圈接地的配电网发生单相接地故障时的特征。为了研究高阻抗故障检测方法,对一种伴随电弧的高阻抗故障以及线路的正常事件进行了仿真,获取了小电阻接地配电网单条线路的线电流样本数据。随后,采用频域分析工具FFT获取了线电流基波与谐波的幅值和相位信息,并设计了一种BP神经网络分类器对电流样本进行分类。最后,利用小波变换这种新的时频分析方法,获取了部分正常事件的特征,与基于FFT和BP神经网络的分类器相结合来降低分类器的误识别率。仿真结果表明,基于FFT和神经网络的分类器可以区分高阻抗故障和线路的正常事件;小波变换与基于FFT和神经网络的分类器相结合的方法能够更准确的识别出电容投切、负载投切和励磁涌流等线路正常事件。更多还原

【Abstract】 With low-resistance grounding method of distribution network being popular in China, high impedance fault detection has attracted people’s attention. Because high impedance fault doesn’t draw enough fault current to operate overcurrent protective devices, it is hard to detect it. It will threaten people’s life or even cause a fire hazard if remains undetected.This paper is financially supported by the Visiting Scholar Program of Chongqing University, aiming to do research on the characteristics and detecting method of high impedance faults. First, the expressions of three phase voltage, fault current and neutral point voltage is derived using symmetrical components method, and the characteristics of single-phase-to-ground fault in low-resistance grounding network and compensated network are compared. Second, for detection method research, line current samples during a kind of high impedance fault accompanied with arcing and normal operations are got through simulation. Then,in frequency domain, FFT is used to get magnitude and phase information of fundamental and harmonic current, and a BP neural network is designed to classify the line current samples. Finally, wavelet transform, the new time-frequency domain analyzing tool, is used to help reduce false classification rate of the FFT and BP neural network based classifier.Simulation results show that the FFT and BP neural network based classifier can distinguish high impedance faults from normal operations. Through combination of wavelet transform and the FFT and BP neural network based classifier, detection of normal operations such as capacitor switching, load switching and inrush current can be more accurate.更多还原