基于ANN的故障分类和定位,为传输系统提供优化的PMU部署
T Malini1, P Thirumoorthi2, K Lakshmi3
1Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India. malinit48@gmail.com.
Scientific reports
|November 7, 2025
概括
本研究介绍了一种人工神经网络 (ANN),用于使用电压数据检测电网故障. 这种新的方法在故障分类和定位方面实现了高精度,提高了电网可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 电网中的人工智能
背景情况:
- 由于动态条件和各种故障类型,现代电网在故障检测方面面临挑战.
- 传统的故障诊断方法受到静态假设和数据要求的限制,阻碍了实时应用.
- 准确及时检测故障对于电网稳定性和运营效率至关重要.
研究的目的:
- 开发一个强大的基于人工神经网络 (ANN) 的框架,用于传输系统中的故障分类和本地化.
- 仅使用总线电压测量用于故障诊断,克服传统技术的局限性.
- 引入一个优化的相位测量单元 (PMU) 放置策略,以提高可观测性和降低硬件成本.
主要方法:
- 开发了一个新的人工神经网络 (ANN) 框架,用于故障分类和本地化.
- 从物理上有意义的特征,从总线电压测量得到,用于增强的稳定性和可解释性.
- 在IEEE 14-bus系统上进行了模拟,以验证拟议的方法.
- 为了优化系统的可观测性,引入了一个新的相位测量单元 (PMU) 放置策略.
主要成果:
- 拟议的基于ANN的方法在故障分类中实现了超过98%的准确性.
- 故障定位是在线长度的2%以下的误差率下进行的.
- 与现有方法相比,ANN方法的准确性高达5.66%,错误率低达30%以上.
- 优化的PMU放置策略提高了可观测性,同时降低了硬件要求.
结论:
- 开发的ANN框架为传输网络的实时故障管理提供了一个可扩展和弹性解决方案.
- 从电压测量中利用物理上有意义的特征可以提高故障诊断的解释性和稳定性.
- 拟议的方法在故障分类和定位方面明显优于现有的数据驱动技术.
- 优化的PMU放置策略有助于更高效,更可靠的电网监控.
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