本文旨在解决采用密度聚类算法分析电力设备位置坐标时,因手动设置超参数导致聚类结果不稳定的问题.通过引入粒子群优化算法,自动调整邻域半径和最小邻域点数,并以轮廓系数作为优化目标,实现了 PSO-DBSCAN(particle swarm optimization for DBSCAN)算法.该算法能够更好地适应不同电力 设备位置坐标的数据特征,提高了聚类分析的准确性和稳定性.实验结果表明,PSO-DBSCAN算法在电力设备位置坐标分析中表现出色,成功克服了传统手动设置超参数的局限性.本研究为电力系统管理提供了一种更智能、自适应的密度聚类分析方法,为电力设备位置坐标分析提供了可靠而高效的解决方案,为电力系统的管理和优化提供了新的思路和工具.
Research on Coordinates of Power Equipment Based on Improved DBSCAN Algorithm
This article aims to address the issue of unstable clustering results in analyzing the coordinates of power equipment locations using density clustering algorithms due to manual setting of hyperparameters.By introducing the particle swarm optimization(PSO)algorithm,automatic adjustment of the neighborhood radius and minimum number of neighboring points is achieved,with the silhouette coefficient as the optimization target,realizing the PSO-DBSCAN(particle swarm optimization for DBSCAN)algorithm.This algorithm better adapts to the data characteristics of different power equipment location coordinates,improving the accuracy and stability of cluster analysis.Experimental results demonstrate that the PSO-DBSCAN algorithm performs excellently in analyzing power equipment location coordinates,successfully overcoming the limitations of traditional manual hyperparameter tuning.This study provides a more intelligent and adaptive density clustering analysis method for power system management,offering a reliable and efficient solution for analyzing power equipment location coordinates,and presenting new ideas and tools for the management and optimization of power systems.
density clusteringpower systemsilhouette coefficientposition coordinatesparticle swarm algorithm
NETL
NSTL
万方数据
维普
