不均衡小样本下多特征优化选择的生命体触电故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.230076

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摘要

针对现有的剩余电流保护装置无法有效识别触电事故的问题,本文提出了一种不均衡小样本下多特征优化选择的生命体触电故障识别方法。首先通过变分自编码器（Variational Auto-Encoders,VAE）对实验收集到的生命体触电小样本数据进行增殖以实现正负样本均衡;接着在时域上提取能够反映波形动态变化特性的23个特征量,并利用高斯核Fisher判别分析（Gaussian Kernel Fisher Discriminant Analysis,GKFDA）与最大信息系数（Maximal Information Coefficient,MIC）法从中选择最优表达特征组;最后,提出基于遗忘因子的在线顺序极限学习机 (Forgetting-factor-based Online Sequential Extreme Learning Machine,FOS-ELM) 算法实现生命体触电行为的鉴别。实验结果表明,所提方法利用不均衡小样本触电数据集就可以训练出一个优秀的分类模型,诊断准确率可达98.75%,诊断时间仅为1.33ms。其优良的性能结合在线增量式学习分类器设计,使得模型具备新知识学习能力,具有极好的工程应用前景。

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关键词 ：剩余电流保护装置, 生命体触电故障, 多特征优化选择, FOS-ELM, 不均衡小样本

Abstract：

The existing residual current device (RCD) operates based on the amplitude of the residual current, but if the threshold is not reasonably set, the RCD is prone to reject or misoperate. Therefore, identifying biological electric-shock faults from grounding faults is a crucial approach. Current research only selects one or several features without following proper feature selection rules. Furthermore, machine learning methods require a certain number of samples to train the model to ensure algorithm accuracy and stability. However, obtaining a large number of biological electric-shock samples is challenging during actual experiments, and the algorithm model cannot learn the waveform in real settings.
To solve the above problems, a biological electric-shock fault identification method based on multi-feature optimization selection under unbalanced small samples is proposed. Firstly, variational auto-encoders (VAE) are adopted to multiply the electric-shock small sample data collected by experiments to achieve positive and negative sample balance. Due to the complexity and danger of the scenes, it is difficult to obtain the actual electric-shock samples. The problem of small samples will lead to low accuracy and poor effectiveness of the training model, and the unbalanced samples will lead to deviations in the prediction results of the model, resulting in poor identification accuracy of a few types of samples. Therefore, a few samples are enhanced by introducing VAE to improve the effectiveness of the model. Secondly, 23 features which can reflect the dynamic characteristics of the waveform are extracted in time domain, the optimal expression feature group is selected from them by Gaussian kernel Fisher discriminant analysis (GKFDA) and maximal information coefficient (MIC). Through data analysis, various index features can be extracted from the changing forms of biological electric-shock waveforms. The addition of high-quality features will improve the diagnostic accuracy of the classifier to a certain extent, but the introduction of bad and redundant features will increase the running time of the algorithm and reduce the diagnostic accuracy of the classifier. Therefore, GKFDA and MIC are combined to perform feature scoring for each feature, and the optimal expression feature group is selected intuitively and independently based on the scoring results, which could improve the feature quality and reflect the regularity of feature selection. Finally, a forgetting-factor-based online sequential extreme learning machine (FOS-ELM) algorithm is investigated to identify the electric-shock behavior. There are abundant electric-shock scenes in the real environments. The escape behaviors of living objects during electric shock will have a great influence on the electric-shock waveform, which makes it difficult for the traditional off-line classifier to have adaptability. The online sequential extreme learning machine (OS-ELM) has an online learning mechanism that allows online updates for new samples without the historical data. The forgetting factor is introduced to form FOS-ELM, aiming to further solve the shortcoming of slow learning speed of OS-ELM, so that it can quickly adapt to changes of environmental samples with higher learning efficiency.
The experimental data of conventional grounding fault and biological electric-shock fault in 12 scenes were collected for the verification of the proposed algorithm. The results show that the diagnosis accuracy of the proposed model can reach 98.75%, among which all 40 conventional grounding fault samples are correctly judged with an accuracy of 100%, while only 1 of 40 actual biological electric-shock fault samples is wrong with an accuracy of 97.5%. From the perspective of time, the average online learning time is 1.378ms, and the average diagnosis time is only 1.33ms.

Key words： Residual current device biological electric-shock fault multi-feature optimization selection FOS-ELM unbalanced small sample

收稿日期: 2023-01-18

PACS:

TM773

基金资助:

福建省自然科学基金资助项目（2021J01633）

通讯作者: 高伟,男,1983年出生,博士,副教授,研究方向为电力系统状态感知及故障抑制。E-mail：gaowei0202@fzu.edu

作者简介: 饶俊民,男,1999年出生,硕士研究生,研究方向为低压配电网电弧故障检测以及低压配电网生命体触电故障检测。E-mail：1986070209@qq.com

引用本文:

高伟, 饶俊民, 全圣鑫, 郭谋发. 不均衡小样本下多特征优化选择的生命体触电故障识别方法[J]. 电工技术学报, 0, (): 230632-230632. Gao Wei, Rao Junmin, Quan Shengxin, Guo Moufa. Biological Electric-Shock Fault Identification Method Based on Multi-feature Optimization Selection under Unbalanced Small Sample. Transactions of China Electrotechnical Society, 0, (): 230632-230632.

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