考虑到新能源发电厂的整合,电力系统的安全和保护
Shengda Wang1, Danni Liu2, Weijia Su1
1JiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation Ltd., Changchun, 130000, China.
Scientific reports
|October 8, 2025
概括
使用自适应中间波器和支向量机的新双算法方法有效地检测和分类与可再生能源集成的现代能源发电厂的电力质量干扰,提高电网稳定性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 整合可再生能源的整合
背景情况:
- 现代能源发电厂越来越多地集成可再生能源 (RES),导致电力质量障碍 (PQD).
- 间歇性RES会导致电压波动,波和瞬态,影响电网的稳定性和可靠性.
- 现有的PQD检测和缓解方法在准确性和速度方面面临挑战.
研究的目的:
- 提出一种新的双算法方法来检测,分类和减轻PQD在基于电网连接的现代能源发电厂系统 (MEPPBS) 中.
- 与现有方法相比,提高PQD识别的准确性和速度.
- 通过集成可再生能源来提高电力系统的弹性和可靠性.
主要方法:
- 使用先进的自适应中间波器 (AMF) 作为信号处理状态观察器.
- 采用基于支持矢量机 (SVM) 的方案来准确识别干扰.
- 训练了SVM分类器的数据集,包括单个和组合的PQD (sags,膨胀,波,中断,瞬态).
主要成果:
- 实现了97%的高检测精度和15毫秒以下的快速响应时间.
- 在区分不同的PQD方面表现出强度,即使使用50%的数据集和20dB的SNR.
- 获得了96%的精度,94%的回忆率和0.04的假阳性率,表明了高可靠性.
结论:
- 拟议的双算法方法 (AMF和SVM) 为MEPPBS中的PQD管理提供了一种新且有效的解决方案.
- 该方法显著提高了PQD检测的准确性和速度,这对电网稳定性至关重要.
- 这些发现支持了这种方法在可再生能源集成电网中增强电力系统弹性方面的潜力.
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