Journal of Sensors

Volume 2015 (2015), Article ID 678120, 19 pages

http://dx.doi.org/10.1155/2015/678120

## Fault Line Selection Method of Small Current to Ground System Based on Atomic Sparse Decomposition and Extreme Learning Machine

^{1}School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China^{2}School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Received 13 October 2014; Accepted 28 November 2014

Academic Editor: Sergiu Dan Stan

Copyright © 2015 Xiaowei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper proposed a fault line voting selection method based on atomic sparse decomposition (ASD) and extreme learning machine (ELM). Firstly, it adopted ASD algorithm to decompose zero sequence current of every feeder line at first two cycles and selected the first four atoms to construct main component atom library, fundamental atom library, and transient characteristic atom libraries 1 and 2, respectively. And it used information entropy theory to calculate the atom libraries; the measure values of information entropy are got. It constructed four ELM networks to train and test atom sample and then obtained every network accuracy. At last, it combined the ELM network output and confidence degree to vote and then compared the vote number to achieve fault line selection (FLS). Simulation experiment illustrated that the method accuracy is 100%, it is not affected by fault distance and transition resistance, and it has strong ability of antinoise interference.

#### 1. Introduction

For small current to ground system, FLS study focus is fault line identified when single phase to ground fault occurred; at this moment, the fault current is weaker, and Petersen coil to ground mode also has the features. Therefore, the conventional method with using current amplitude size and phase information is difficult to obtain satisfactory results.

In recent years, modern signal processing technology used FLS to get fault characteristic information, such as wavelet transform [1], transform [2], mathematical morphology [3], Hilbert-Huang transform (HHT) [4], Prony algorithm [5], and Hough transform [6]. Besides, the common method of FLS criterion had artificial neural networks [7], support vector machines [8], and Bayesian classification [9].

Zero sequence current was decomposed by wavelet transform and calculated wavelet modulus maxima to determine arrival time of traveling wave’s head and then compared the amplitude and polarity of every feeder line at this time to achieve FLS [1]. It used transform to get modulus value and phase angle of every frequency range and compared modulus value and phase angle to obtain characteristic frequency and voting mechanism, respectively; the experiments indicated that the method could not only judge the fault line accurately but also obtain the FLS confidence degree [2]. Paper [10] used transform to get transient fault feature, and, based on the frequency point of transient maximum energy, it chose characteristic frequency sequence; therefore, the criterion with relative entropy values of multiple combination modes determined the fault section. Paper [3] proposed a novel method which was based on mathematical morphology; the method included two aspects: one used morphological filters to preprocess the data and removed the noise impact for FLS at the maximum extent and the other adopted morphological operators to detect the denoised signal with mutant aspect to judge the fault line. Paper [4] calculated transient instantaneous power by Hilbert-Huang transform (HHT) and got fault direction well; the method took advantage of transient high-frequency component at lower sampling rate. It tried to divide the zero sequence current signal into several segments to ensure good continuity and smaller mutation at every subsegment, and Prony algorithm was applied to choose transient dominant component with maximum energy principle and then calculated the relative entropy and voted by preliminary vote and values check to judge the fault line [5]. Hough transform was adopted to construct whole mutant direction angle which indicated overall trend of zero sequence current at initial stage, and FLS was achieved by distinguishing the direction angle [6]. Paper [7] replaced ordinary neurons with rough neurons and fuzzy neurons to identify 10 kinds of fault type; the method improved the training speed and reduced training samples and fault identification accuracy was enhanced. It used correlation coefficient of zero sequence voltage and charge as characteristic input to construct FLS process which was based on transient zero sequence - features; the method adopted support vector machine algorithm with small sample [8]. For incomplete information of fault diagnosis, [9] adopted evidence uncertainty reasoning and compared abnormal events to reduce computation amount.

This paper proposed a novel FLS method which was based on combination of ASD and ELM. Firstly, it used atomic sparse algorithm to decompose zero sequence current of every feeder line and extracted the first four atoms to construct fault sample library, respectively; besides, it calculated information entropy measure of every library. Then, it trained the ELM network to improve network output accuracy. At last, fault voting was adopted to vote every feeder line and compared the values, and then the fault line was judged. Simulation results showed that the accuracy rate of proposed method is 100% and had strong ability of antinoise interference.

The remaining of this paper is organized as follows. In Section 2, we analyzed the physical characteristics of zero sequence current. In Sections 3 and 4, the theory of time-frequency atom decomposition and ELM work principle are presented, respectively. In Section 5, test signals analysis is given in the paper. In Section 6, we chose the characteristic atoms of zero sequence current. In Section 7, the FLS methods are proposed. In Section 8, example analysis is applied to verify the proposed method. In Section 9, we discussed the applicability of the method. In Section 10, the paper is completed with conclusions and future directions.

#### 2. Physical Characteristics Analysis

Transient zero sequence circuit of single phase to ground fault is shown in Figure 1, where and are zero sequence capacitance and inductance, respectively, is transition resistance of grounding point, and are equivalent resistance and inductance of arc suppression coil, and is zero sequence voltage.