基于机器学习的激发冲击下的单相变压器中磁场的预测
Qingjun Peng1, Hantao Du2, Zezhong Zheng2
1Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650127, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种机器学习模型,用于预测变压器在激发过程中的磁场,以确保智能电网的稳定性. 深度神经网络 (DNN) 模型提供了准确和快速的预测,增强了操作监控.
科学领域:
- 电气工程 电气工程
- 计算电磁学 计算机电磁学
- 在电力系统中的人工智能
背景情况:
- 智能电网需要实时监控电力设备状态.
- 变压器激发在冲动时对电力系统稳定性构成重大风险.
- 像有限元分析 (FEA) 这样的传统方法对于冲进预测是耗时的.
研究的目的:
- 开发和验证一种机器学习方法,用于在激发过程中预测变压器的内部磁场.
- 为了能够准确和及时监控智能电网中的变压器运行状态.
- 为传统的磁场分析方法提供更快的替代方案.
主要方法:
- 实现深度神经网络 (DNN) 模型用于磁场预测.
- 对DNN模型预测准确性的实验验证.
- 预测速度与有限元素分析 (FEA) 方法的比较.
主要成果:
- 对于磁场预测,DNN模型实现了4.02%的平均绝对百分比误差 (MAPE).
- 单个磁场数据点的平均预测时间为0.41秒.
- DNN模型显示出速度优势是FEA的46.68倍.
结论:
- 机器学习,特别是DNN,对于在激发冲动期间预测变压器磁场是有效的.
- 与FEA相比,拟议的方法显著加快了磁场预测.
- 这种方法提高了智能电网实时监控和稳定的运行能力.
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