现代电力2025，Vol.42Issue(2)：385-392,8.DOI:10.19725/j.cnki.1007-2322.2023.0029
基于模糊C聚类-云模型-LSTM网络的分接开关气象易损性评估预警
Meteorological Vulnerability Assessment and Warning of On-load Tap Changer Based on Fuzzy-C Means-cloud Model-LSTM Network
龚禹璐 1崔龙飞 2陈静 1王典浪 1须雷 2李东 1尹启1
作者信息
- 1. 中国南方电网有限责任公司超高压输电公司曲靖局,云南省 曲靖市 655000
- 2. 南京南瑞继保工程技术有限公司,江苏省 南京市 210000
- 折叠
摘要
Abstract
To address the issue of severe damage caused by meteorological disasters and fault potential,a vulnerability as-sessment and early warning method for on load tap changer(OLTC)of transformer in the new power system was proposed based on improved cloud model and long short term memory network(LSTM).Firstly,the assessment system of disaster-causing factors was established based on the meteorological monitoring data of OLTC.According to the FCM clustering al-gorithm,the threshold division of the traditional cloud model was improved to obtain the objective cloud model,and con-sequently a combination cloud model was constructed by com-bining the subjective and objective cloud models.Based on the natural disaster theory,the disaster indicators to be evaluated are dynamically adjusted by considering the factors such as the geographical disaster pregnant environment,the disaster resist-ance ability of the equipment itself,the accumulation degree of disaster risk and the comprehensive risk processing ability fa-cing the disaster.These modified indicators were calculated and utilized in the membership degree of the combined cloud mod-el,aiming to acquire the disaster vulnerability level for OLTC.Finally,the LSTM neural network was applied to extract the as-sociation rules between each disaster causing factor and each disaster vulnerability,thereby facilitating meteorological dis-aster early warning and forming the optimal response strategy.The example results indicate that the OLTC meteorological dis-aster vulnerability assessment and early warning method pro-posed in this paper exhibits high accuracy and effectively achieves the objective of disaster prevention and reduction.关键词
有载分接开关/极端气象灾害/改进云模型/LSTM神经网络/易损性评估预警Key words
on-load tap changer/extreme meteorological disasters/improved cloud model/LSTM neural network/vulnerability assessment and early warning分类
动力与电气工程引用本文复制引用
龚禹璐,崔龙飞,陈静,王典浪,须雷,李东,尹启..基于模糊C聚类-云模型-LSTM网络的分接开关气象易损性评估预警[J].现代电力,2025,42(2):385-392,8.
