基于PSO-KNN的变电站设备三维点云识别方法研究  

3D point cloud research on an identification method based on PSO-KNN substation equipment

作　　者：李科[1] LI Ke(State Grid Sichuan Information&Telecommunication Company(Provincial Data Center),Chengdu 610072,China)

机构地区：[1]国网四川省电力公司信通公司(省数据中心),四川成都610072

出　　处：《电力系统保护与控制》2021年第18期182-187,共6页Power System Protection and Control

基　　金：国家电网公司科技项目资助(5216A0182020R)。

摘　　要：针对传统的三维重建方法因数据缺失而造成的精度差、效率低等问题,在三维激光扫描点云的基础上,提出了一种将粒子群优化算法和k-近邻分类算法相结合的变电站设备三维点云识别方法。通过粒子群优化算法对各子空间特征的系数权重进行优化,k-近邻分类算法完成分类。通过实验分析点云子空间的大小和丢失率对识别效果的影响,并与改进的迭代最近点算法进行比较,验证该方法的优越性和准确性。实验结果表明,该方法具有较好的识别准确率和效率,识别准确率达到95%以上,平均识别时间为0.19 s,具有一定的应用价值。There are problems of poor accuracy and low efficiency caused by the lack of data in traditional three-dimensional reconstruction methods.Thus,based on a three-dimensional laser scanning point cloud,a recognition method for substation equipment is proposed.This combines the particle swarm optimization algorithm and the k-nearest neighbor classification algorithm.The particle population optimization algorithm is used to optimize the coefficient weight of each subspace feature,and the k-nearest neighbor classification method is used to classify the equipment.The influence of the size and loss rate of the point cloud subspace on the recognition effect is analyzed through experiment.It is compared with the improved iterative closest point algorithm to verify the superiority and accuracy of the method.Experimental results show that this method can effectively improve recognition accuracy and efficiency.The recognition accuracy can reach more than 95%,and the average recognition time is 0.19 seconds,which has application value.

关 键 词：变电站设备 粒子群算法 k-近邻分类算法 三维点云识别 三维重建 

分 类 号：TM63[电气工程—电力系统及自动化]