基于GWO优化的双通道MA方法的变压器故障识别
1Skill Training Center of State Grid Jiangsu Electric Power Co., Ltd., Suzhou, China.
PloS one
|October 28, 2024
概括
本研究介绍了一种新的灰狼优化器 (GWO) 优化的双通道多层感知器 (MLP) 注意力模型,用于准确的变压器故障识别. 与现有技术相比,拟议的方法显著提高了诊断准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 计算智能是一种计算智能.
背景情况:
- 变压器故障识别对于电力系统可靠性至关重要.
- 现有的方法往往缺乏足够的准确性,需要先进的诊断方法.
- 自然启发的算法为优化复杂的识别模型提供了潜力.
研究的目的:
- 使用灰狼优化器 (GWO) 优化的双通道多层感知器 (MLP) 注意力模型开发精确的变压器故障识别方法.
- 通过利用混合人工智能技术,提高变压器故障诊断的性能.
- 评估拟议的GWO优化模型与传统方法的有效性.
主要方法:
- 构建一个双通道模型,整合注意力机制 (AM) 和MLP.
- 使用GWO算法优化双通道MLP-Attention模型的隐藏层结构.
- 使用数字动态实时模拟器 (DDRTS) 系统模拟典型的变压器故障.
主要成果:
- 该GWO优化的方法实现了变压器故障识别准确率在95.3%至96.7%之间.
- 观察到显著的精度改进:14.1%比BP,9.6%比SVM,9.3%比MLP和3.3%比单通道MA模型.
- 实验验证证证实了拟议方法的优越性.
结论:
- GWO优化的双通道MLP-Attention模型是用于变压器故障识别的合理和有效方法.
- 这种方法为功率变压器的诊断准确性提供了显著的改进.
- 该研究强调了将自然启发的优化与用于关键基础设施监控的深度学习相结合的潜力.
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