基于PSO-SOM神经网络算法的串联电弧故障检测
Detection of Series Arc Faults Based on PSO-SOM Neural Network Algorithm
贾金伟 1王闻燚 1徐梓源 1戴军瑛 1俞玲 1李启本1
作者信息
- 1. 国网上海市电力公司松江供电公司,上海 201600
- 折叠
摘要
自组织特征映射(SOM)神经网络是一种无监督式学习的竞争神经网络,具有灵活性强、聚类结果可视化等优点,但是在需要区分的类别较多且不同类别数据的特征差异不明显时,SOM的聚类效果可能并不好,分类准确率也会下降.提出了一种利用粒子群优化(PSO)算法对SOM网络的权值进行优化的解决方法,将PSO-SOM算法、常规SOM算法以及学习向量量化(LVQ)算法神经网络分别应用于电弧故障检测.仿真结果表明,经过PSO优化的SOM神经网络的检测准确率可达95.00％以上,而未经优化的SOM神经网络与LVQ神经网络的准确率仅为50％左右.
Abstract
Self-Organizing Maps(SOM)neural networks are unsupervised learning competitive neural networks known for their flexibility and visual clustering results.However,SOM's clustering performance may degrade when dealing with a large number of categories or when the feature differences between different classes of data are not obvious.To address this issue,a Particle Swarm Optimization(PSO)algorithm is proposed to optimize the weights of the SOM network.The PSO-SOM algorithm,conventional SOM algorithm,and Leaning Vector Quantization(LVQ)algorithm are applied to arc fault detection.Simulation results demonstrate that the accuracy of the PSO-optimized SOM network can reach over 95.00％,while the accuracy of the unoptimized SOM net-work and LVQ network is around 50％.
关键词
串联电弧/故障检测/粒子群优化算法/自组织特征映射神经网络Key words
series arc/fault detection/particle swarm optimization algorithm/self-organizing maps neural networks引用本文复制引用
出版年
2024
NETL
NSTL
万方数据