双向门式反复单元神经网络用于中压直流系统的故障诊断和快速维护
Bohyung Lee1,2, Yeseul Kim2, Hyunyong Lee3
1Department of Electrical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一种新的双向门式反复流动单元 (Bi-GRU) 模型准确地检测和定位中压直流 (MVDC) 系统中的故障. 这种先进的故障诊断方法确保了电网稳定性和可靠性,即使有噪音的传感器数据.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能在能源中的作用
背景情况:
- 增加可再生能源的整合需要强大的中压直流 (MVDC) 系统稳定性.
- MVDC系统故障可能导致严重的干扰,需要快速准确的故障诊断.
- 传统的故障检测算法在复杂的故障场景和实时处理中面临挑战.
研究的目的:
- 为MVDC系统开发一个先进的故障诊断模型.
- 分类和定位各种故障类型,包括极点对极点 (PTP),正极点对地面 (PPTG) 和负极点对地面 (NPTG) 的故障.
- 提高MVDC系统的可靠性和实时诊断能力.
主要方法:
- 实现双向门式反复单元 (Bi-GRU) 神经网络模型.
- 利用电压信号的时间行为进行故障分析.
- 对卷积神经网络 (CNN) 和双向长期短期记忆 (Bi-LSTM) 网络进行模型性能测试.
主要成果:
- Bi-GRU模型在故障分类和定位方面实现了95.54%的整体准确性.
- 平均故障检测时间低于1.3毫秒,符合实时要求.
- 该模型表现出高的诊断准确性和稳定性,即使在人工引入的传感器噪声下.
结论:
- 拟议的Bi-GRU模型与MVDC故障诊断的传统算法相比,提供了更高的性能.
- 开发的方法有效地处理多个故障场景,并在噪音条件下保持准确性.
- 这有助于显著提高MVDC电源系统的安全性,可靠性和运行可靠性.
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