A fault location method of underground distribution network
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摘要: 针对采用随机选取法、K-均值聚类法确定RBF神经网络隐含层节点中心和宽度只能得到局部最优解、基本粒子群优化算法易发生早熟收敛且对于某些函数优化精度差的问题，提出了将惯性权重模型和收敛因子模型相结合的改进的粒子群优化算法；针对煤矿井下配电网发生单相接地故障后定位困难、传统的故障测距方法存在可靠性差、测距精度低的问题，提出了采用改进的粒子群优化算法优化RBF神经网络进行井下配电网单相接地故障测距的方法。仿真结果表明，经改进的粒子群优化算法优化的RBF神经网络的测距精度高于RBF神经网络，能够实现故障点的准确、可靠定位。Abstract: In view of problems that it can only obtain local optimal solution by using random choice method and K-means clustering method to determine center and width of nodes of hidden layer of RBF neural network, and PSO algorithm is easy to premature convergence and has bad precision for some functions, the paper proposed an improved PSO algorithm which combines with inertia weight model and convergence factor model. In view of problems of difficult positioning for single-phase grounding fault in underground distribution network, bad reliability and low precision existed in traditional fault location methods, the paper proposed a single-phase grounding fault location method of underground distribution network by using the improved PSO algorithm to optimize RBF neural network. The simulation results show that location precision of RBF neural network optimized by the improved PSO algorithm is higher than RBF neural network, and can realize accurate and reliable location for fault point.
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期刊类型引用(3)
1. 孔德彪，夏秋龙. 郭屯煤矿智能化工作面建设与应用. 晋控科学技术. 2024(02): 40-42 . 
百度学术
2. 马艾强，姚顽强. 煤矿井下移动机器人多传感器自适应融合SLAM方法. 工矿自动化. 2024(05): 107-117 . 本站查看
3. 黄晨烜，常健，王雷. 基于激光雷达的井下带式输送机边缘提取方法. 工矿自动化. 2024(09): 115-123 . 本站查看
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