Abstract - In order to model and visualize the performance of lightning and its protection system, several analytical methods such as the rolling sphere method (RSM) have been developed in which protected and unprotected areas are separated. However, in none of these methods, the scale and severity of the damage can be quantified numerically. In this paper, by using the leader progression model (LPM) in 3D space along with an intelligent algorithm named Teaching-Learning-Based optimization (TLBO), a new method is proposed to identify the vulnerable areas with the highest probability of lightning strikes. In using the TLBO algorithm, a new method for determining the critical range of current is defined and the most vulnerable points on the structure are determined, so that, based on the simulation results, the best positions of the lightning rods can be specified respectively. A sample asymmetric structure of height less than 60 meters is analyzed as a case study, and the optimal locations and heights of lightning rods are determined using the rolling sphere method and the proposed method, respectively. The simulation results validate the effectiveness of the proposed method which makes it suitable for other complex structures.

中文翻译：

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一种评估复杂非对称结构防雷系统性能的新型智能方法

摘要 - 为了对雷电及其保护系统的性能进行建模和可视化，已经开发了几种分析方法，例如滚球法（RSM），其中将受保护区域和未受保护区域分开。然而，在这些方法中，没有一种方法可以用数字量化损坏的规模和严重程度。在本文中，通过使用 3D 空间中的先导级数模型 (LPM) 以及称为基于教学的优化 (TLBO) 的智能算法，提出了一种识别雷击概率最高的脆弱区域的新方法。在使用 TLBO 算法时，定义了一种确定电流临界范围的新方法，并确定了结构上最脆弱的点，以便根据仿真结果，可以分别指定避雷针的最佳位置。以高度小于60米的非对称结构样例为例，分别采用滚球法和提出的方法确定避雷针的最佳位置和高度。仿真结果验证了该方法的有效性，使其适用于其他复杂结构。