基于故障区域识别和证据信息融合的转换站故障诊断方法
Shuzheng Wang1, Xiaoqi Wang1, Xuchao Ren2
1Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
准确的故障诊断在直流转换器站是非常重要的. 这项研究提出了一种新方法,将交换机和电气数据融合起来,可靠地确定故障,提高系统稳定性.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 控制系统 控制系统
背景情况:
- 直流转换器站对于输电至关重要,但由于高压和复杂的设备,容易发生故障.
- 现有的故障诊断方法在有限的信息上扎,影响了电力系统和活跃的配电网络的稳定性.
- 准确的故障分析对于这些关键基础设施组件的安全和稳定运行至关重要.
研究的目的:
- 开发用于直流转换器站的先进故障诊断方法.
- 通过整合多个数据源来提高故障检测的准确性和范围.
- 提高直流传输系统和接收端网络的可靠性和稳定性.
主要方法:
- 一种新的故障诊断方法,将初步故障范围识别与融合证据信息相结合.
- 序列事件记录 (SER) 数据的预处理和统计分析以缩小故障位置.
- 使用模糊的培养网和BP神经网络与实时数字模拟 (RTDS) 数据进行证据提取.
- 应用D-S (Dempster-Shafer) 证据理论来接开关信号和电量,以进行全面的诊断.
主要成果:
- 拟议的方法可靠且准确地识别转换站保护区内的故障点.
- 与单一信息方法相比,交换机和电气证据信息的融合显著提高了诊断精度.
- 使用来自直流转换器站的故障数据证明了有效性.
结论:
- 开发的故障诊断方法对直流转换器站的传统方法有了显著的改进.
- 基于开关和电气数据的证据融合提高了故障定位的可靠性.
- 这种方法有助于积极的分销网络的稳定和安全运行.
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