| 引用本文: | 王海龙,何 山,胡 帅,等.基于小波变换与TKEO的故障识别与定位方法研究[J].电力系统保护与控制,2025,53(14):177-186.[点击复制] |
| WANG Hailong,HE Shan,HU Shuai,et al.Research on fault identification and location method based on wavelet transform and TKEO[J].Power System Protection and Control,2025,53(14):177-186[点击复制] |
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| 摘要: |
| 在含有储能的光伏制氢系统中，故障类型的准确识别及精确定位对其安全稳定运行具有重要意义。针对短距离直流输电系统故障识别与定位精准度低、快速性差的问题，提出一种基于小波变换(wavelet transform, WT)和Teager-Kaiser能量算子(Teager-Kaiser energy operator, TKEO)的双端行波故障测距方法。首先对故障信号进行小波变换，使用软阈值进行去噪并重构信号。提取并分析每个小波分解层的特征信息，根据高低频分解层能量的比值判定故障类型。其次采用TKEO法提取出经小波分解后的瞬时能量谱，精确标定首个波头到达直流线路两端的采样点。然后采用双端测距法精确求解出故障距离。最后在Matlab/Simulink中搭建光伏直流制氢系统进行仿真验证。结果表明，所提方法对故障类型的识别和定位具有较高的准确性。 |
| 关键词: 直流微电网 故障识别 Teager-Kaiser能量算子 故障定位 |
| DOI：10.19783/j.cnki.pspc.241313 |
| 投稿时间：2024-09-28修订日期：2025-02-06 |
| 基金项目:新疆维吾尔自治区重点研发项目资助(2022B01003-3)；新疆维吾尔自治区重点实验开放课题项目资助(2023D04029)；国家自然科学基金项目资助(52266018) |
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| Research on fault identification and location method based on wavelet transform and TKEO |
| WANG Hailong1,HE Shan1,2,HU Shuai3,WANG Weiqing1,2 |
| (1. Key Laboratory of Renewable Energy Power Generation and Grid-connected Technology in the Autonomous Region,
Xinjiang University, Urumqi 830017, China; 2. Engineering Research Center of Renewable Energy Power Generation
and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi 830017, China;
3. State Grid Xinjiang Electric Power Research Institute, Urumqi 830011, China) |
| Abstract: |
| In photovoltaic hydrogen production systems equipped with energy storage, accurate identification and precise localization of faults are critical for ensuring safe and stable operation. To address the issues of low accuracy and poor rapidity in fault identification and localization in short-distance DC transmission systems, a dual-terminal traveling wave fault location method based on wavelet transform (WT) and Teager-Kaiser energy operator (TKEO) is proposed. First, the fault signal undergoes wavelet transform, denoised using soft thresholding, and then reconstructed. Feature information of each wavelet decomposition layer is extracted and analyzed, and the fault type is determined based on the energy ratio between high- and low-frequency decomposition layers. Subsequently, the TKEO method is applied to extract the instantaneous energy spectrum from the wavelet-decomposed signal, accurately identifying the sampling points where the first wavefront arrives at both ends of the DC line. A dual-terminal location method is then employed to precisely calculate the fault distance. Finally, the proposed method is validated through simulations in a photovoltaic-based DC hydrogen production system modeled in MATLAB/Simulink. The results demonstrate that the proposed method achieves high accuracy in both fault identification and localization. |
| Key words: DC microgrid fault identification Teager-Kaiser energy operator fault location |
