【摘要】 为了更有效地研究配电线路杆塔在不同地形地貌下遭受雷害风险概率的大小,提出一种基于减法聚类与ANFIS的分类识别模型。通过对配电线路的历史雷电数据进行采集与分析,研究雷害活动与线路杆塔所处地形地貌之间的相互关系,确定了7个影响杆塔遭受雷击概率的特征参数,并将其作为模型的输入向量。采用减法聚类算法确定聚类中心和聚类个数,通过ANFIS对已知配电线路易击杆塔地形地貌的特征数据进行分析,最终实现了对易击杆塔地形地貌的分类识别。仿真结果表明,该系统可以较好地识别出不同地形地貌下杆塔遭受雷击概率的大小,能够对山区防雷提供一定的参考和借鉴。更多还原

【Abstract】 In order to more effectively study the probability of the transmission line towers suffering from lightning risks under different topography,this paper proposes a classification and recognition model based on subtractive clustering and ANFIS.Through the collection and analysis of historical lightning data of distribution lines,this paper studies the relationship between lightning activity and the topography and topography of the line tower,7 characteristic parameters that affect the probability of lightning strikes on the towers are determined and used as input vector of the model.The subtractive clustering algorithm is used to determine the cluster center and the number of clusters,and the characteristic data of the topography of the transmission towers that are prone to lightning strikes are analyzed through ANFIS,and the classification and recognition of the topography of the transmission towers that are prone to lightning strikes is realized.The simulation results show that the system can better identify the probability of lightning strikes on poles and towers under different terrains.更多还原