使用Christiano-Fitzgerald随机步行过器进行绝缘体故障预测的数据处理的组方法
Stefano Frizzo Stefenon1,2, Laio Oriel Seman3, Nemesio Fava Sopelsa Neto4
1Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
使用Christiano-Fitzgerald随机步行 (CFRW) 和组数据处理 (GMDH) 的新混合方法通过分析泄漏电流,准确地预测电网绝缘体的故障. 这种方法通过早期故障检测来提高电源供应的可靠性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 电力供应系统的破坏性故障往往先于分布绝缘体中泄漏电流的增加.
- 监测绝缘体的健康状况对于保持电力发电网的可靠性至关重要.
研究的目的:
- 开发和评估一种新的混合方法,用于使用泄漏当前时间序列数据预测受污染的电网绝缘体的故障.
- 评估克里斯蒂亚诺-菲茨杰拉德随机步行 (CFRW) 过器和组数据处理 (GMDH) 方法用于故障预测的有效性.
主要方法:
- 开发了一种混合故障预测方法,将Christiano-Fitzgerald随机步行 (CFRW) 过器用于趋势分解和组数据处理 (GMDH) 方法用于时间序列预测.
- 在高压实验室模拟中,对15kV级绝缘体进行了受控污染,在28小时内记录了泄漏电流,直到闪过.
- 在减少非线性方面,CFRW过器的性能与使用移动平均线的季节性分解进行了比较.
主要成果:
- 与使用移动平均线的季节性分解相比,CFRW过器在减少非线性方面表现优异.
- 拟议的CFRW-GMDH混合方法实现了3.44×10-12的根平均平方误差,用于故障预测.
- 在预测绝缘器故障方面,CFRW-GMDH方法超过了标准的GMDH和长短期记忆 (LSTM) 模型.
结论:
- 混合CFRW-GMDH方法是基于泄漏电流数据预测电网绝缘体故障的高效和有前途的工具.
- 这种方法为电力公司提供了一种可靠的方法来监测绝缘体健康状况并预测故障,从而提高电力供应的可靠性.
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