相关实验视频
莫肯:基于多尺度变压器的Gating KAN专家网络用于时间序列预测
Donghyun Kim1, Jimyung Kang2, Hoseong Hwang1
1Department of Medical Artificial Intelligent, Eul-Ji University, Seongnam-si 13135, Republic of Korea.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
一个新的基于专家混合的科尔摩戈罗夫-阿诺德网络 (MoEKAN) 模型准确地预测负载自动切换器 (OLTC) 振动信号. 这种先进的诊断工具改善了电力变压器的状态监测和预测性维护.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 装载式水龙头变换器 (OLTC) 是电力变压器的重要组件.
- 来自OLTC的振动信号提供了对设备健康状况和潜在故障的见解.
- 准确预测这些信号对于预测性维护至关重要.
研究的目的:
- 引入一种新的MoEKAN模型,用于OLTC振动信号的增强时间序列预测.
- 提高OLTC状态监测的准确性和可靠性.
主要方法:
- 开发了一个基于专家混合的科尔摩戈罗夫-阿诺德网络 (MoEKAN) 模型.
- 集成的可逆实例规范化 (RevIN) 为数据分布适应性.
- 采用基于变压器的门锁机制,用于动态专家集成.
- 利用多级信号处理来捕获复杂的信号模式.
主要成果:
- 摩肯模型在实际的OLTC运营数据上表现出优异的预测性能.
- 实现了较低的预测错误:MSE为133.4579,MAE为7.2801和MAPE为4.4272. 这些都是低预测错误.
- 在准确性方面表现优于所有基准比较模型.
结论:
- 莫肯模型是OLTC振动信号预测的实用和有效工具.
- 证实了该模型对OLTC可靠状态监测和预测性维护的潜力.
- 突出了先进的人工智能模型在电力系统诊断中的重要性.
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