Detection of Series Arc Faults Based on PSO-SOM Neural Network Algorithm
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％.
series arcfault detectionparticle swarm optimization algorithmself-organizing maps neural networks
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