基于SGMD近似和优化BILSTM的功率变压器部分放电故障诊断
Haikun Shang1, Zixuan Zhao1, Jiawen Li1
1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
本研究引入了对功率变压器的高级部分放电 (PD) 故障诊断方法,提高了准确性和可靠性. 这种新的方法实现了98.6%的诊断率,超过了噪声敏感度降低的传统技术.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 功率变压器的部分放电 (PD) 故障对电网稳定性构成重大风险.
- 传统的PD故障诊断方法往往受到低准确度和高噪音灵敏度的影响.
- 准确的PD故障诊断对于主动维护和防止灾难性故障至关重要.
研究的目的:
- 开发一种新且高度准确的PD故障诊断方法,用于电力变压器.
- 改善现有诊断技术的局限性.
- 为了提高功率变压器操作的可靠性和安全性.
主要方法:
- 综合几何模式分解 (SGMD) 与近似 (ApEn) 结合用于主导PD特征提取.
- 优化双向长短期记忆 (BILSTM) 神经网络,增强了金子优化 (GJO) 以提高诊断准确性.
- 对特征提取性能进行SGMD与FFT,EMD和VMD的比较分析.
主要成果:
- 与FT,EMD和VMD相比,SGMD-ApEn在提取占主导地位的PD特征方面表现优越.
- 拟议的GJO优化的BILSTM方法显著提高了PD故障识别准确度.
- 实验验证证了新方法对传统方法的有效性和优越性.
结论:
- 新的SGMD-ApEn和GJO-BILSTM方法为电力变压器的PD故障诊断提供了一个非常有效的解决方案.
- 拟议的技术实现了98.6%的高诊断率,提高了对噪声的强度.
- 这一进步通过改进的故障检测,有助于更安全,更稳定的功率变压器运行.
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