基于变化模式分解和随机配置网络的水电发电机组振动趋势的多步预测模型
Shaokai Yan1, Fei Chen1, Jiandong Yang1
1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一种新的多步预测模型,用于水力发电机组振动趋势. 该模型通过准确预测振动变化,提高了运行状态维护和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
背景情况:
- 轮机振动监测对于水电厂的运行效率和维护至关重要.
- 预测振动趋势有助于主动操作状态维护.
研究的目的:
- 开发和验证水电机组振动趋势的多步预测模型.
- 提高水力发电机组振动预测的准确性和可靠性.
主要方法:
- 变化模式分解 (VMD) 用于将振动信号分解为内在模式函数 (IMF).
- 随机配置网络 (SCN) 用于预测IMF的各个组成部分.
- 一种用于整合多步预测预测的递归策略.
主要成果:
- 提出的模型准确地预测了水力发电机组的振动趋势.
- 使用VMD进行分解有效地稳定了振动信号.
- 综合方法在不同的单元组件中表现出高的预测准确性.
结论:
- 多步预测模型为水电厂的运营策略提供了显著的好处.
- 准确的振动趋势预测支持优化控制策略和提高运营效率.
- 这种方法有助于改善水电单元的维护和性能.
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