机器学习算法用于电力分布系统的电压稳定性评估
Molla Addisu Mossie1, Tefera Terefe Yetayew2, Girmaw Teshager Bitew3
1Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia. mollaaddisu2@gmail.com.
Scientific reports
|August 30, 2025
概括
机器学习模型准确地预测埃塞俄比亚电网的电压稳定性. 渐变增强和随机森林提供快速评估, 识别网络弹性增强的关键公共汽车.
科学领域:
- 电气工程
- 计算智能
背景情况:
- 电压不稳定是电力系统的一个关键问题,限制了运行能力和传输.
- 传统的电压安全评估方法是计算密集的,阻碍了实时应用.
- 机器学习为高效准确的电压稳定性分析提供了一个有希望的替代方案.
研究的目的:
- 开发和评估用于预测埃塞俄比亚配电网络中的快速电压稳定指数 (FVSI) 的机器学习模型.
- 为了比较线性回归,随机森林,梯度增强和支向量机的性能,以评估电压稳定性.
- 在研究的电力系统中确定具有高不稳定性风险的关键总线.
主要方法:
- 应用线性回归,随机森林,梯度提升和支向量机模型.
- 在埃塞俄比亚35辆公共汽车和53辆公共汽车的配送网络中预测FVSI在名义和变量负载条件下 (10-150%).
- 使用R2和根平均平方误差 (RMSE) 度量分析模型准确性并执行FVSI值分析.
主要成果:
- 梯度提升 (GB) 和随机森林 (RF) 模型显示出更高的准确性 (R2分别为0.9998和0.999).
- GB型号在低RMSE值 (例如,53公交系统的0.0002) 中获得了最高的精度.
- 在两个系统中确定特定的总线为需要立即监测的关键不稳定性风险点.
结论:
- 集成机器学习方法,特别是GB和RF,对于快速的电压稳定性评估非常有效.
- 这项研究成功地确定了有针对性的干预措施的关键领域,以提高电网的弹性.
- 准确的实时电压稳定性预测对于防止发电网的电压崩至关重要.
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