基于深度神经网络算法和无气味卡尔曼过的微电网抗岛保护方案
Sohaib Tahir Chauhdary1, Taha Saeed Khan2, Saad Arif3
1Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Sultanate of Oman, Salalah, 211, Oman. sohaibchauhdary@hotmail.com.
Scientific reports
|August 6, 2025
概括
这项研究引入了一种新型的微电网抗岛屿保护 (MAIP) 方法,使用无气质卡尔曼过 (UKF) 和深度神经网络 (DNN). 该方法有效地以高准确度检测岛屿事件,确保微电网安全.
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:
- 微电网需要强大的防岛保护 (MAIP) 来实现安全可靠的运行.
- 现有的MAIP方法在各种条件下准确检测岛屿事件时面临挑战.
研究的目的:
- 提出和验证一个创新的MAIP方案,利用无气味卡尔曼过 (UKF) 和深度神经网络 (DNN).
- 为了提高检测准确度,并减少微电网中岛屿事件的非检测区域.
主要方法:
- 拟议的方法使用UKF作为状态观察员来分析分布式发电 (DG) 终端或共同合点 (PCC) 的电压信号.
- 深度神经网络 (DNN) 通过将UKF估计的电压与测量的PCC电压进行比较来计算DNN残余 (DNNR) 指数.
- 当DNNR指数超过预定义的值时,就会检测到岛屿事件.
主要成果:
- 在平衡和不平衡负载生成场景中,MAIP方案在检测岛屿事件方面表现出了很高的有效性.
- 该方法成功地区分了岛屿和非岛屿事件.
- 在IEEE UL174测试台上的模拟证实了MAIP方案的快速运行和高精度 (98.5%).
结论:
- 基于UKF-DNN的MAIP方案为微电网安全提供了可靠和准确的解决方案.
- 拟议的方法显著减少了非检测区和计算负担.
- 这种方法提高了微电网运行的整体稳定性和安全性.
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