| 引用本文: | 杨新宇,赵庆生,韩肖清,梁定康,王旭平.基于IF-AD-ELM的特高压直流输电系统故障辨识[J].电力系统保护与控制,2024,52(8):1-9.[点击复制] |
| YANG Xinyu,ZHAO Qingsheng,HAN Xiaoqing,LIANG Dingkang,WANG Xuping.Fault identification of a UHVDC transmission system based on IF-AD-ELM[J].Power System Protection and Control,2024,52(8):1-9[点击复制] |
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| 摘要: |
| 针对现有的特高压直流(ultra high voltage direct current, UHVDC)输电系统故障检测方法灵敏度低、难以识别高阻接地故障的问题，提出了一种基于整数因子(integer factor, IF)-近似导数(approximate derivative, AD)和极限学习机(extreme learning machine, ELM)的特高压直流输电系统故障辨识方法。其中整数因子用于分析不同采样频率下的信号，近似导数法用于获得信号不同程度的细节系数。首先，基于不同的整数因子对信号进行下采样，并利用近似导数法对所得信号求一阶、二阶和三阶近似导数。其次，分别计算各个子信号的熵特征。然后，用基于交叉验证的递归特征消除(recursive feature elimination with cross validation, RFECV)算法对得到的一系列特征进行特征筛选，并结合ELM对特高压直流输电系统进行故障辨识。最后，在Matlab/Simulink环境中搭建了±800 kV的UHVDC系统模型，模拟不同故障类型。实验结果表明，所提方法在识别特高压直流输电系统不同类型故障时有更高的准确率，且耐受过渡电阻能力强。 |
| 关键词: 特高压直流 下采样 特征选择 极限学习机 故障辨识 |
| DOI：10.19783/j.cnki.pspc.231036 |
| 投稿时间：2023-08-12修订日期：2024-01-24 |
| 基金项目:国家自然科学基金项目资助(51777132)；国家自然科学青年基金项目资助(51907138)；国网山西省电力公司科技项目资助(520510220002) |
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| Fault identification of a UHVDC transmission system based on IF-AD-ELM |
| YANG Xinyu,ZHAO Qingsheng,HAN Xiaoqing,LIANG Dingkang,WANG Xuping |
| (Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China) |
| Abstract: |
| There is a problem of low sensitivity and difficulty in identifying high-resistance ground faults in existing fault detection methods for ultra high voltage direct current (UHVDC) transmission system. Thus a fault identification method for a UHVDC transmission system based on the integer factor (IF)-approximate derivative (AD) and an extreme learning machine (ELM) is proposed. The IF is used to analyze the signals at different sampling frequencies, and the AD method is used to obtain different degrees of detail coefficients for the signals. First, the signal is down-sampled based on different IFs, and the AD method is used to calculate the first, second and third order approximate derivatives of the obtained signal. Secondly, the entropy characteristics of each sub-signal are calculated. Then, recursive feature elimination with a cross validation (RFECV) algorithm is used to screen the features of the obtained series of features, and the ELM is used to identify the UHVDC transmission system fault types. Finally, the UHVDC system model of ±800 kV is built in the Matlab/Simulink environment to simulate different fault types. The experimental results show that the proposed method has higher accuracy and strong tolerance to transition resistance when identifying different types of faults in a UHVDC transmission system. |
| Key words: UHVDC down-sampling feature selection ELM fault identification |
