一种基于 taxicolor优化的多支向量方法,用于变压器故障诊断
Yong Ding1, Weijian Mai1, Zhijun Zhang2
1School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
概括
一种新的Swarm Budorcas Taxicolor基于优化的多支持向量 (SBTO-MSV) 方法提高了变压器故障检测的准确性. 这种先进的技术实现了98.1%的准确性,超过了可靠的电力系统操作的现有方法.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 变压器故障对电力系统可靠性构成重大风险.
- 准确的故障检测对于防止级联故障和确保电网稳定性至关重要.
- 现有的变压器故障诊断方法往往在识别准确度低的情况下扎.
研究的目的:
- 开发一种用于变压器故障检测的新且高度准确的方法.
- 提高变压器故障诊断模型的分类性能.
- 为提高电力系统的运行可靠性提供强大的技术解决方案.
主要方法:
- 开发了一个多支向量 (MSV) 模型,用于使用溶解气体分析数据对变压器故障进行多重分类.
- 在训练期间使用Swarm Budorcas Taxicolor Optimization (SBTO) 算法来优化MSV模型参数.
- 拟议的SBTO-MSV方法整合了优化和分类,以实现有效的变压器故障诊断.
主要成果:
- 在IEC TC 10数据集上,SBTO-MSV方法实现了最高平均准确率98.1%.
- 实验结果显示,与传统和最先进的机器学习算法相比,其性能明显优于传统的机器学习算法.
- 对其他数据集的验证证实了该模型的优秀分类性能和概括能力.
结论:
- SBTO-MSV方法为变压器故障诊断提供了卓越的性能.
- 对于机器学习模型来说,SBTO算法展示了出色的参数搜索能力.
- 这一进步为增强变压器故障检测和确保电力系统可靠性提供了强大的技术支持.
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