混合人工智能和半导体方法用于提高电源质量
Ravikumar Chinthaginjala1, Asadi Srinivasulu2,3, Anupam Agrawal4
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Scientific reports
|July 15, 2025
概括
深度学习模型,特别是LSTM,通过准确识别和预测诸如电压下降和波等问题,显著提高电力质量. 结合传统和数据驱动方法的混合系统为智能电网提供了适应性解决方案.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电力质量对于电网的稳定性和可靠性至关重要.
- 挑战包括电压下降,膨胀,波和短暂的干扰.
- 传统的控制方法与复杂,动态的电力质量问题作斗争.
研究的目的:
- 开发和评估一种新的方法来提高电力质量,使用机器学习 (ML) 和深度学习 (DL).
- 为了比较各种ML和DL算法的性能,识别和预测功率质量干扰.
- 研究融合传统和数据驱动控制策略的混合系统的有效性.
主要方法:
- 实施数据驱动框架,将传统控制与自适应的ML/DL模型相结合.
- 测试算法包括支持向量机 (SVM),随机森林,神经网络,卷积神经网络 (CNN) 和长短期记忆 (LSTM).
- 利用实时数据进行模拟和现实世界的实验.
主要成果:
- 深度学习模型,特别是LSTM,在电源质量问题检测和预测方面表现出卓越的准确性和可靠性.
- 美国有线电视新闻网达到91.8%的精度,而LSTM达到100%的精度和94.5%的回忆.
- 传统的ML模型面临着不平衡数据集的挑战,导致精度和回忆能力低于DL模型.
结论:
- 集成传统和数据驱动控制策略的混合系统显示出适应性和可靠的电力质量管理的前景.
- 深度学习模型为复杂的电力质量场景提供了更高的准确性,这对于未来的智能电网应用至关重要.
- 实际部署需要平衡计算需求和解决数据不平衡的挑战.
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01:24Fast Decoupled and DC Powerflow
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