A Cumulative Standard Deviation Sum Based Method for High Resistance Fault Identification and Classification in Power Transmission Lines

Mohammed H. H. Musa; Zhengyou He; Ling Fu; Yujia Deng

doi:10.1186/s41601-018-0102-4

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A Cumulative Standard Deviation Sum Based Method for High Resistance Fault Identification and Classification in Power Transmission Lines

Original Research | 更新时间：2026-09-30

- A Cumulative Standard Deviation Sum Based Method for High Resistance Fault Identification and Classification in Power Transmission Lines
- A cumulative standard deviation sumbased method for high resistance faultidentification and classification in powertransmission lines
- 现代电力系统保护与控制（英文） 2018年3卷第3期页码：1-12
- 作者机构：
  
  1. Department of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, People's Republic of China
  2. Sudanese Thermal Power Generating Company, Khartoum,Sudan
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-018-0102-4
  中图分类号：
- 纸质出版：2018-07-01
- 稿件说明：
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Mohammed H. H. Musa, Zhengyou He, Ling Fu, 等. A Cumulative Standard Deviation Sum Based Method for High Resistance Fault Identification and Classification in Power Transmission Lines[J]. 现代电力系统保护与控制（英文）, 2018,3(3):1-12.

Mohammed H. H. Musa, Zhengyou He, Ling Fu, et al. A cumulative standard deviation sumbased method for high resistance faultidentification and classification in powertransmission lines[J]. Protection and Control of Modern Power Systems, 2018, 3(3): 1-12.
Mohammed H. H. Musa, Zhengyou He, Ling Fu, 等. A Cumulative Standard Deviation Sum Based Method for High Resistance Fault Identification and Classification in Power Transmission Lines[J]. 现代电力系统保护与控制（英文）, 2018,3(3):1-12. DOI： 10.1186/s41601-018-0102-4.

Mohammed H. H. Musa, Zhengyou He, Ling Fu, et al. A cumulative standard deviation sumbased method for high resistance faultidentification and classification in powertransmission lines[J]. Protection and Control of Modern Power Systems, 2018, 3(3): 1-12. DOI： 10.1186/s41601-018-0102-4.

摘要

High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification. In this paper

the standard deviation and accumulation method are employed to perform the fault detection and classification. It is primarily built in two stages. Firstly

the standard deviations for the measured current's signals of the local and remote terminals is computed to extract the fault feature. Secondly

the cumulative approach is used to enlarge the fault feature to perform the high resistance fault. The proposed scheme is known as Standard Deviation Index (SDI)

and it is obtained for the three phases and zero sequence. The proposed algorithm has been tested through different fault circumstances such as multiple faults locations

fault resistances

and fault inception time. Moreover

far-end faults with high-resistance

faults happened nearby the terminal

faults considering variable loading angle

sudden load change

different sampling frequency

bad signaling and a fault occurred in the presence of series compensation are also discussed. The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.5\Omega$ and can be detected within a millisecond after the fault inception. Additionally

the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

Abstract

High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification. In this paper

the standard deviation and accumulation method are employed to perform the fault detection and classification. It is primarily built in two stages. Firstly

the standard deviations for the measured current’s signals of the local and remote terminals is computed to extract the fault feature. Secondly

the cumulative approach is used to enlarge the fault feature to perform the high resistance fault. The proposed scheme is known as Standard Deviation Index (SDI)

and it is obtained for the three phases and zero sequence. The proposed algorithm has been tested through different fault circumstances such as multiple faults locations

fault resistances

and fault inception time. Moreover

far-end faults with high-resistance

faults happened nearby the terminal

faults considering variable loading angle

sudden load change

different sampling frequency

bad signaling and a fault occurred in the presence of series compensation are also discussed. The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩ and can be detected within a millisecond after the fault inception. Additionally

the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

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