为了提高配电网理论线损计算精度，提出一种基于复合学习算法的配电网理论线损计算模型。该模型将配电网理论线损计算抽象成多元回归问题，将理论线损计算的各类影响因素和理论线损值分别作为多元回归问题的输入向量和输出向量，并构造样本集输入到复合学习算法中加以训练，以得到配电网理论线损计算模型。复合学习算法由广义回归神经网络完成样本集训练，并在训练过程中利用粒子群算法动态地搜索广义回归神经网络最优训练参数，从而降低了理论线损计算模型的误差。实验结果显示，与传统方法相比基于复合学习算法的配电网理论线损计算模型具有更高的计算精度。

In order to improve the calculation accuracy of theoretical line loss for distribution system, a novel calculation method based on hybrid learning algorithm(HLA), which is composed by generalized regression neural network(GRNN) and particle swarm optimization(PSO), is proposed. The theoretical line loss calculation is abstracted into the multi regression problem (MRP), of which the input and output vector are selected as various impact factors and theoretical line loss values. The sample set is constructed for HLA training. In GRNN training process, the method can overcome the difficulty for selecting optimal training parameters by PSO. The experiments show that, the HLA method has better performance compared with the traditional one.