首页|融合Canopy的K-means算法在电力设备故障检测中的应用研究

融合Canopy的K-means算法在电力设备故障检测中的应用研究

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为提高故障检测方法的准确性和效率,研究将Canopy算法与K-均值算法进行融合,对基于簇的局部离群因子算法进行改良,构建改良电力设备故障检测算法.对改良电力设备故障检测算法进行实证分析,发现该算法的运算时间为118.3 s,故障检测正确率为0.91,优于其他对比算法.综上,研究提出的改良电力设备故障检测算法可提高故障检测准确性与计算效率,可为电力设备故障检测领域的研究和实践提供参考,为智能电网的建设提供理论依据.
Application of K-means algorithm fused with Canopy in fault detection of power equipment
In order to improve the accuracy and efficiency of the fault detection method,the Canopy algorithm and the K-mean algorithm are integrated to improve the cluster-based local outlier factor algorithm and improve the fault detection algorithm of power equipment.The empirical analysis of the improved fault detection algorithm of power equipment shows that the operation time of the algorithm is 118.3 s and the fault detection accuracy is 0.91,which is better than other comparison algorithms.In conclusion,the improved fault detection algorithm of power equipment can improve the accuracy and calculation efficiency of fault detection,and can provide reference for the research and practice in the field of power equipment fault detection.

canopy algorithmk-means algorithmpower equipmentfault detectionperformance improvement

朱愈、张耘

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中国电信股份有限公司景德镇分公司,江西景德 333036

国家电投集团江西电力有限公司景德镇发电厂,江西景德 333036

Canopy算法 K-means算法 电力设备 故障检测 性能提升

2024

自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
年,卷(期):2024.(4)
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