Due to the continuous appearance of safety fault accidents in the practice process, operation safety has become the central task of various operation and management tasks of the power grid. Therefore, to establish a line overload identification and data control model for the power system, we first defined the vulnerability of complex power systems based on the analysis of each line and node. For finding the optimal parameters of this model, we proposed an improved optimization strategy by combining the genetic algorithm and BP neural network. To verified the effectiveness of our proposed method, we conducted experiments on a simulation on the IEEE 30‐node power system environment. Experimental results demonstrate that the proposed algorithms can establish an optimized overload identification model with better performance. This study can help to conduct reasonable adjustment when overload happens to the power system, and then reduce similar failure as well as enhance the operation safety.

中文翻译：

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基于改进神经网络算法的电力系统线路过载识别研究

由于实践过程中安全故障事故不断出现，安全运行成为电网各项运行管理任务的中心任务。因此，为建立电力系统线路过载识别和数据控制模型，我们首先在分​​析各线路和节点的基础上，定义复杂电力系统的脆弱性。为了寻找该模型的最优参数，我们提出了一种结合遗传算法和BP神经网络的改进优化策略。为了验证我们提出的方法的有效性，我们对 IEEE 30 节点电力系统环境进行了仿真实验。实验结果表明，所提出的算法可以建立一个性能更好的优化过载识别模型。