研究高压断路器的故障识别与语音打印信息特征的研究
Sihao Wang1, Yongrong Zhou1, Zhaoxing Ma2
1State Grid Electric Power Research Institute Co., Ltd., Nanjing, 211106, China.
Scientific reports
|April 23, 2024
概括
本研究引入了一种使用语音印记数据的高压断路器的新型故障识别方法. 该技术在检测故障方面达到92%以上的准确性,确保可靠的电力系统运行.
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:
- 高压断路器对于电力系统的稳定性至关重要.
- 操作语音印记信号包含有价值的诊断信息.
- 现有的故障检测方法可能缺乏准确性或稳定性.
研究的目的:
- 提出并验证使用语音印记数据对高压断路器的故障识别方法.
- 利用先进的信号处理和机器学习来提高诊断能力.
- 提高电力系统运行的可靠性和安全性.
主要方法:
- 使用专用设备从高压断路器获取语音打印信息.
- 应用改进的S变换来从语音印记数据生成振幅矩阵.
- 在时频域中使用单值分解 (SVD) 提取故障特征.
- 开发和培训用于故障诊断的支持矢量机 (SVM) 模型.
主要成果:
- 拟议的方法成功地从语音印记信号中提取故障特征数量.
- 训练有素的SVM模型在识别断路器故障方面取得了很高的准确性.
- 实验模拟表明,在各种操作条件下,准确率为92.6%.
- 该方法显示出良好的准确性和稳定性,验证了其实际适用性.
结论:
- 语音打印信息是用于高压断路器故障诊断的丰富数据源.
- 改进的S变换,SVD和SVM的综合方法提供了有效的故障识别解决方案.
- 开发的方法提高了电网运行的可靠性和安全性.
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