聚类分析
断层(地质)
相似性(几何)
亲和繁殖
计算机科学
断路器
可靠性(半导体)
样品(材料)
理论(学习稳定性)
鉴定(生物学)
数据挖掘
模式识别(心理学)
功率(物理)
工程类
人工智能
机器学习
模糊聚类
电气工程
物理
地质学
图像(数学)
植物
树冠聚类算法
量子力学
地震学
色谱法
化学
生物
标识
DOI:10.1016/j.ijepes.2019.105651
摘要
Online condition monitoring and fault diagnosis of circuit breakers (CBs) is a significant method to effectively improve the stability and reliability of the power system. However, the currently used fault diagnosis method still have certain defects including the inability to identify unknown faults for training samples. Therefore, this paper proposes an evolving method for fast and accurate online fault diagnosis of CBs. On the basis of collecting samples of CB trip/close coil current (CC) features, an optimized affinity propagation (AP) clustering algorithm to accurately extract the sample clustering exemplars is presented. Additionally, operating state identification and fault diagnosis of CBs is carried out by calculating the similarity coefficient between the new sample and exemplars online. Diagnosis of unknown faults is also achieved by introducing the threshold and comparing it with similarity coefficient results. Simulation results prove that the proposed method can precisely identify various known CBs faults and has the ability to recognizes unknown CBs fault samples even when the number of training samples is small, providing a foundation for CB fault location and condition-based maintenance.
科研通智能强力驱动
Strongly Powered by AbleSci AI