基于IWHO优化的MS1DCNN算法和LIF频谱的变压器故障诊断研究
Pengcheng Yan1,2,3, Fengxiang Chen2, Xuyue Kan2
1State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China.
Analytical methods : advancing methods and applications
|July 17, 2023
概括
这项研究引入了激光诱导光 (LIF) 系统与多尺度一维卷积神经网络 (MS1DCNN) 结合,以实现更快,更准确的变压器故障诊断,提高电网可靠性.
科学领域:
- 电气工程 电气工程
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 变压器故障威胁到电网的可靠性.
- 传统的诊断方法耗时且不太准确.
- 先进的诊断技术对于稳定的电源供应至关重要.
研究的目的:
- 开发一个高效和准确的变压器故障诊断系统.
- 克服现有诊断方法的局限性.
- 通过智能诊断来提高电网运行的可靠性.
主要方法:
- 激光诱导光 (LIF) 从变压器油中获取光谱数据.
- 使用标准正常变量 (SNV) 和多重分散校正 (MSC) 的数据预处理.
- 通过线性区分分析 (LDA) 和内核主要组件分析 (KPCA) 减少尺寸.
- 使用多尺度一维卷积神经网络 (MS1DCNN) 进行故障分类,并由改进的野生马优化器 (IWHO) 优化.
主要成果:
- 与传统方法相比,拟议的LIF技术在数据提取中提供了更高的效率和精度.
- MSC展示了卓越的预处理,KPCA在缩小维度方面表现出色,MS1DCNN显示了更好的预测,IWHO提供了有效的优化.
- 综合的MSC-KPCA-IWHO-MS1DCNN模型获得了优异的诊断性能,MSE为4.9037 × 10−4,MAE为0.0179,R2为0.9996.
结论:
- 开发的系统为变压器故障诊断提供了一个高度准确和高效的方法.
- 对变压器故障的智能诊断对于持续和稳定的电网运行至关重要.
- 综合方法显著推进了电力系统诊断领域.
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