研究基于光光谱分析和SBOA优化的变压器故障诊断方法的研究 BPNNN
Xueqing Chen1, Dacheng Li2,3, Anjing Wang2,3
1Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新的光分析方法用于变压器故障诊断,改进了传统的溶解气体分析 (DGA). 开发的Loess-MDS-SBOA-BP模型准确地预测了变压器故障的持续时间,提高了诊断能力.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
背景情况:
- 传统的溶解气体分析 (DGA) 用于变压器故障检测在早期诊断方面存在局限性.
- 光分析为变压器绝缘油故障诊断提供了一个有希望的替代方案.
- 现有的智能算法主要专注于定性故障类型诊断,缺乏定量分析.
研究的目的:
- 开发一种使用光光谱学的变压器绝缘油的定量故障诊断方法.
- 建立基于光谱数据的变压器故障持续时间的预测模型.
- 克服DGA和现有的定性光分析方法的局限性.
主要方法:
- 在模拟间隔放电条件下从绝缘油中收集光谱数据.
- 应用了光谱预处理技术 (例如,洛斯光滑) 和缩小维度 (例如,MDS).
- 开发并优化了使用粒子群优化 (PSO) 和秘书鸟优化算法 (SBOA) 与反向传播神经网络 (BPNN) 的变压器故障诊断模型.
主要成果:
- 洛斯-MDS-SBOA-BP模型表现出卓越的性能,达到99.711%的确定系数 (R2) 和0.27144.4的根平均平方误差 (RMSE).
- 该模型准确地预测了变压器故障的持续时间,预测值与实验结果密切匹配.
- 综合评估证实了与其他测试的算法相比,拟议模型的最佳性能.
结论:
- 开发的基于光分析的模型为变压器故障诊断提供了准确和定量方法.
- 该模型预测故障持续时间的能力为及时维护和预防提供了显著的优势.
- 这项研究为通过先进的光谱分析和智能算法推进变压器故障检测技术奠定了基础.
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