摘要
针对配电系统继电保护设备终端负荷不断增容会导致设备故障发生概率不断加大的问题,提出基于决策树的继电保护设备故障运行状态自动检测方法。基于决策树算法构建故障自动诊断模型,利用网络训练分类器和回归预测器等机器学习算法,以提高诊断效率和准确性,并自动检测继电保护设备的故障运行状态。结果表明,与传统方法相比,该方法能快速准确地识别故障位置,证明决策树算法在继电保护设备故障运行状态自动检测中具有较高的准确性。
Aiming at the problem that the increasing terminal load of relay protection equipment in distribution systems leads to an increasing probability of equipment failure,a decision tree based automatic detection method for the fault operation status of relay protection equipment is proposed.Building a fault automatic diagnosis model based on decision tree algorithm,utilizing machine learning algorithms such as network trained classifiers and regression predictors to improve diagnostic efficiency and accuracy,and automatically detect the fault operation status of relay protection equipment.The results show that compared with traditional methods,the proposed method can quickly and accurately identify the location of faults,proving that the decision tree algorithm has high accuracy in automatic detection of fault operation status of relay protection equipment.
作者
吴园园
杨琼琼
WU Yuanyuan;YANG Qiongqiong(State Grid Baoji Power Supply Company,Baoji,Shaanxi 721001,China)
出处
《自动化应用》
2024年第11期227-228,231,共3页
Automation Application
关键词
决策树算法
运行状态检测
故障检测
继电保护设备
decision tree algorithm
running state detection
fault detection
relay protection equipment
