Power System Fault Diagnosis Based on Artificial Fish-Swarm Algorithm-Reference-Cited by-同舟云学术

Power System Fault Diagnosis Based on Artificial Fish-Swarm Algorithm

Published:2014-07 Issue: Volume:986-987 Page:1541-1544
ISSN:1662-8985
Container-title:Advanced Materials Research
language:
Short-container-title:AMR

Author:

Qu Tian Yi¹

Affiliation:

1. Xuzhou Institute of Technology

Abstract

The primary method of power system fault diagnosis is that the fault section estimation is demonstrated a 0-1 programming model according to the acting theory of protective relays. This paper proposed a kind improved artificial fish-swarm algorithm: binary artificial fish-swarm algorithm (BAFA) for this kind of problem. Forward speed of optimal direction between swarm-behavior and follow-behavior is analyzed and a comparative analysis is made with genetic algorithm (GA) on this basis. Results indicate that follow-behavior is superior to swarm-behavior on forward speed of optimal direction and BAFA is superior to GA on the comprehensive performance. Meanwhile, it is shown that BAFA has the fast convergence speed and better optimization ability.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Link

https://www.scientific.net/AMR.986-987.1541.pdf

Reference6 articles.

1. T. Sakquchi et al. Prospects of expert systems in power system operation[J]. Electrical Power & Energy Systems，1988，10(2).

2. Cho H J，Park J K. An expert system for fault section diagnosis of power systems using fuzzy relations[J]. IEEE Trans on Power Systems, 1977, 12(1): 342-348.

3. Li Ran，Li Jinghua，Su Lijun. Application of rough set to build electric power grid fault diagnosis model based on decision tree[J]. Relay, 2005, 33(18): 1-5.

4. Wang Jialin，Xia Li，Wu Zhengguo，Yang Xuanfang. State of arts of fault diagnosis of power system[J]. Power System Protection and Control, 2010, 38(18): 210~214.

5. Zang Tianlei，He Zhengyou，Li Chaowen，Qian Qingquan. Fault section estimation in transmission network based on binary swarm intelligence algorithm[J]. Power System Protection and Control, 2010, 38(14): 16~22.