基于数据质量提升的变压器顶部油温的预测模型
Naxin Dong1, Ruoyu Zhang2, Zong Li2
1Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
The Review of scientific instruments
|July 26, 2023
概括
本研究引入了一种新型模型,通过提高数据质量和分解复杂的时间序列数据,准确预测变压器顶部油温 (TOT). 拟议的方法显著提高了预测准确度,这对于变压器维护和事故预防至关重要.
科学领域:
- 电气工程 电气工程
- 预测性维护是指预测性维护.
- 数据科学数据科学数据科学
背景情况:
- 最高油温 (TOT) 是变压器健康和运行状态的关键指标.
- 在线监测中缺少的数据和混合的时间尺度信息阻碍了准确的TOT预测.
- 由于数据的复杂性,现有的单个模型难以达到所需的准确性.
研究的目的:
- 为准确的变压器TOT预测提出数据质量增强模型.
- 为应对缺失值和多尺度时间数据所带来的挑战.
- 提高变压器运行和维护计划的可靠性.
主要方法:
- 利用马尔科夫模型在在线监控数据中赋值缺失的值.
- 使用集体实证模态分解 (EEMD) 来分离混合时间尺度信息.
- 开发了使用极端学习机器 (ELM) 和重建结果的子预测模型.
主要成果:
- 在为期两天的TOT预测中,获得了5.27%的平均绝对百分比误差 (MAPE) 和2.46的根平均平方误差 (RMSE).
- 与BP神经网络和支持矢量机 (SVM) 模型相比,显著提高了准确性.
- 与基准模型相比,MAPE降低了高达69.56%,RMSE降低了高达67.02%.
结论:
- 拟议的数据质量增强模型有效地预测了变压器TOT.
- 该方法成功地处理了缺失的数据和复杂的时间动态.
- 这种方法为变压器故障诊断和在线监控系统提供了至关重要的支持.
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