| 引用本文: | 熊晓祎,肖先勇,赵恒.基于自适应算法的触电事故电流检测[J].电力系统保护与控制,2017,45(4):139-144.[点击复制] |
| XIONG Xiaoyi,XIAO Xianyong,ZHAO Heng.Adaptive algorithm based electrical shock current detection method[J].Power System Protection and Control,2017,45(4):139-144[点击复制] |
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| 摘要: |
| 针对如何快速有效检测触电事故电流的问题，给出了一种基于自适应算法的触电事故电流检测方法。基于自适应滤波原理，建立了自适应触电电流检测模型，并结合归一化最小均方(N-LMS)算法对测量的总剩余电流噪声消除和自然剩余电流分离，实现了触电事故电流的检测。利用触电实验的实测数据对该方法进行验证，并与增量检测法、BP神经网络检测法、最小二乘支持向量机(LS-SVM)检测法以及电流幅值检测法进行比较分析。仿真结果表明，基于自适应算法的触电事故电流检测方法具有响应时间短、噪声鲁棒性好并且能有效消除保护动作死区的优点，对新一代剩余电流保护装置开发有一定的参考价值。 |
| 关键词: 剩余电流保护技术 触电事故电流 N-LMS自适应算法 事故检测 自适应模型 |
| DOI：10.7667/PSPC160315 |
| 投稿时间：2016-03-10修订日期：2016-04-18 |
| 基金项目:四川省科技支撑计划(2015RZ0055) |
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| Adaptive algorithm based electrical shock current detection method |
| XIONG Xiaoyi,XIAO Xianyong,ZHAO Heng |
| (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China) |
| Abstract: |
| Aimed at the problem of detecting electrical shock current fast and correctly, this paper proposes an N-LMS adaptive algorithm based electrical shock detection method. According to the principle of adaptive filter, it establishes adaptive electrical shock current detection model, and then applies adaptive filters to eliminate the influence of noise and normal residual current in turns from the measured total residual current detected and separates the electrical shock current out. Finally, the proposed method is testified by the electrical shock experimental data which proves the validity of the method. Compared with the increment method, BP neural network based method, LS-SVM based method, and present current-amplitude based method for RCDs, the results conclude that the proposed method has short response time and great noise robusticity and eliminates protection dead-zone. In short, the method could help develop novel RCDs. This work is supported by Science and Technology Department of Sichuan Province, China (No. 2015RZ0055). |
| Key words: residual current protection technology electrical shock current normal least mean square fault detection adaptive model |
