摘要
继电保护二次回路涉及众多线路和设备,故障检测效率较低,为此,设计一种基于模糊均值聚类的继电保护二次回路故障检测方法。采集继电保护二次回路的状态数据,应用模糊均值聚类算法来处理这些数据,通过算法的智能分类功能,将具有相似特征的故障模式准确归类;基于分类后的故障特征,构建二次回路故障检测模型,通过模型的输出结果,实现对继电保护二次回路故障的精准定位。结果表明,基于模糊均值聚类的继电保护二次回路故障检测方法的故障检测效率均在94%以上,波动不超过1%,证明设计方法检测效率较高,能有效实现继电保护二次回路故障检测。
The secondary circuit of relay protection involves numerous lines and equipment,and the fault detection efficiency is low.Therefore,a fault detection method for the secondary circuit of relay protection based on fuzzy mean clustering is designed.Collect status data of the secondary circuit of relay protection,apply fuzzy mean clustering algorithm to process this data,and accurately classify fault modes with similar characteristics through the intelligent classification function of the algorithm.Based on the classified fault characteristics,a secondary circuit fault detection model is constructed,and through the output results of the model,accurate positioning of secondary circuit faults in relay protection is achieved.The results show that the fault detection efficiency of the relay protection secondary circuit fault detection method based on fuzzy mean clustering is above 94%,with fluctuations not exceeding 1%.This proves that the design method has high detection efficiency and can effectively achieve fault detection of the relay protection secondary circuit.
作者
郑昕恺
陈梓荣
ZHENG Xinkai;CHEN Zirong(State Grid Quanzhou Power Supply Company,Quanzhou,Fujian 362000,China)
出处
《自动化应用》
2024年第16期81-83,共3页
Automation Application
关键词
模糊均值聚类
继电保护
二次回路
故障检测
深度学习
fuzzy mean clustering
relay protection
secondary circuit
fault detection
deep learning
