IGA优化的神经网络计算配电网理论线损  

Calculation of Line Losses in Distribution Systems Using Artificial Neural Network Aided by Immune Genetic Algorithm

作　　者：李秀卿[1] 赵丽娜[1] 孟庆然[1] 王兴东 孙志政 

机构地区：[1]东北电力大学电气工程学院,吉林132012 [2]鸡西供电公司,鸡西158100

出　　处：《电力系统及其自动化学报》2009年第5期87-91,共5页Proceedings of the CSU-EPSA

摘　　要：针对BP神经网络学习速度慢、容易陷入局部极小的缺点,提出了一种基于免疫遗传算法(IGA)的人工神经网络(artificial neural network,ANN)计算配电网的理论线损。该算法在遗传算法(genetic algorithm,GA)的基础上引入生物免疫系统中的多样性保持机制和抗体浓度调节机制,有效地克服了GA算法的搜索效率低、个体多样性差及早熟现象,扩大了神经网络的权值搜索空间,提高了网络系统的学习效率和精度。实例计算结果表明,同混合遗传算法相比,该算法具有较快的收敛速度和较强的全局收敛性能,比现有其他计算配网线损的方法更为准确。In this paper, a method for calculating the line losses in distribution systems is presented using artificial neural network（ANN） aided by immune genetic algorithm（IGA）, so as to overcome the shortcomings that back propagation neural networks （BPNN） is easily converged at a local minimum and the training process is slow. The mechanisms of diversity maintaining and antibody density regulation in a biological immune system are introduced into IGA based on genetic algorithm （GA）. The proposed algorithm overcomes the problems of GA such as low search efficiency, bad individual diversity and premature enlarges the search space of weight and improves the training efficiency and precision of neural networks. Simulation results show that the BP neural networks designed by IGA have better performance in convergent speed and global convergence compared with hybrid genetic algorithm and that the method is more accurate than other ones.

关 键 词：免疫遗传算法 人工神经网络 BP模型 配电网 理论线损 

分 类 号：TM72[电气工程—电力系统及自动化]