基于PIO-VMD-EDOO的煤炭切割机的切割单元中的电源线路的故障诱导检测方法
Yue Wang1,2,3, Xiang Rong4,5, Han Shi4,5
1CCTEG Changzhou Research Institute, Changzhou, 213015, China. LB23230005@cumt.edu.cn.
Scientific reports
|January 20, 2026
概括
这项研究引入了煤炭剪刀电线的新型故障检测方法,在噪音条件下提高了准确性. 灵感来自于Pigeon的优化-变化模式分解-信封衍生运算符 (PIO-VMD-EDO) 方法提高了故障位置的可靠性.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 电力系统 电力系统
背景情况:
- 煤炭切削机电线显示出高阶波和非线性负载的噪声.
- 这些线路的断层会产生微弱的瞬态,被现有的噪音所掩盖,难以检测.
- 准确的故障位置对于煤矿的运行可靠性至关重要.
研究的目的:
- 开发一套先进的故障检测和定位方法,用于煤炭剪切机电源供应线.
- 解决故障期间高阶波,噪声和短暂信号所带来的挑战.
- 为了提高复杂的电力系统中故障位置的准确性和可靠性.
主要方法:
- 一种新的故障检测方法,集成了Pigeon Inspired Optimization (PIO),变化模式分解 (VMD) 和信封衍生运算符 (EDO).
- 利用模糊 (FE) 作为PIO的优化目标,以适应性地分解电力线路的移动波.
- 采用增强的EDO机制进行波面校准和信号放大.
主要成果:
- 与DWT,CWT,HHT和EMD相比,PIO-VMD-EDO方法显示出更高的故障位置精度.
- 在故障位置实现了不超过2.5%的最大相对误差.
- 该方法在不同的故障条件,噪声干扰和采样速率下被证明是可靠的.
结论:
- 拟议的PIO-VMD-EDO方法为煤矿电网中的故障位置提供了高精度和可靠性.
- 它在具有非线性负荷和功率电子设备的环境中特别有效.
- 这种技术显著提高了在具有挑战性的电源系统中故障定位的可靠性.
关键词:
煤矿电力供应系统 煤矿电力供应系统煤炭剪裁机 煤炭剪裁机包裹衍生工具运营商 (EDO)错误的位置 错误的位置子启发的优化 (PIO)变化模式分解 (VMD) 是一种变化模式分解.波浪正面校准的时间更多相关视频
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