| 引用本文: | 齐郑,张惠汐,饶志,李志,张首魁.基于极限学习机的多信息融合区段定位方法[J].电力系统保护与控制,2014,42(19):74-80.[点击复制] |
| QI Zheng,ZHANG Hui-xi,RAO Zhi,LI Zhi,ZHANG Shou-kui.Multi-information fusion fault location based on extreme learning machine[J].Power System Protection and Control,2014,42(19):74-80[点击复制] |
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| 摘要: |
| 对于小电流接地系统单相接地故障定位问题，现有方法单一，实际运行时准确性低，难以满足现场需要。提出一种基于极限学习机的多信息融合区段定位方法。故障发生后，终端对实时测得的零序暂态电流运用暂态能量法、小波法、首半波法提取特征向量上传给主站，输入到经训练后得到权重参数的极限学习机网络中，主站启动多信息融合定位算法并输出区段定位结果。该方法受接地位置、接地时刻、接地过渡电阻等因素的影响较小，对不同的单相接地情况适用性强，具有较高的区段定位鲁棒性。通过现场实际验证，证明了该定位方法的可行性。 |
| 关键词: 小电流接地系统 单相接地 暂态分量 极限学习机 多信息融合 |
| DOI：10.7667/j.issn.1674-3415.2014.19.012 |
| 投稿时间：2013-12-30修订日期：2014-05-08 |
| 基金项目: |
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| Multi-information fusion fault location based on extreme learning machine |
| QI Zheng,ZHANG Hui-xi,RAO Zhi,LI Zhi,ZHANG Shou-kui |
| (North China Electric Power University, Beijing 102206, China; Electric Power Energy Saving Education Ministry Engineering Research Center, Beijing 102206, China) |
| Abstract: |
| Fault location methods for single-phase-to-earth in small current neutral grounding system are difficult to meet the needs of the site, which is too simple to have a high accuracy in actual run-time. This paper presents a fault location method of multi-information fusion based on extreme learning machine. For real-time measured zero-sequence transient current, the terminal uses transient energy method, wavelet method, and the first half-wave method to extract feature vector and uploads it to the host station; inputs to the extreme learning machine network which has the be-trained parameters. Host station restarts multi-information fusion fault location and output search result. The method is less affected by ground position, grounding moment, ground transition resistance and other factor, is applicable to different single-phase ground case, which has higher fault location robustness. The on-site experiments are carried out to prove the feasibility of the method. |
| Key words: small current neutral grounding system single-phase-to-earth fault transient component extreme learning machine multi-information fusion |
