基于自组织竞争网络与RPROP算法的线损计算研究  

The Research on Line Loss Calculation of RPROP Algorithm Based on Self-Organizing Competitive Network

作　　者：张艳 徐卫锋 ZHANG Yan;XU Wei-feng(State Grid Shanghai Shinan Electric Power Supply Company,Shanghai 200233,China)

机构地区：[1]国网上海市电力公司市南供电公司,上海200233

出　　处：《电工电气》2022年第7期31-34,44,共5页Electrotechnics Electric

摘　　要：为更好地发现高效的降损措施,并为科学地制定线损目标提供依据,提出了一种基于自组织竞争神经网络的RPROP神经网络的线损计算方法。RPROP神经网络确保了网络在有限的训练次数下能够收敛,利用自组织竞争神经网络对信息数据进行有效分类,提高了RPROP神经网络的输出精度。通过在MATLAB平台进行仿真实验,并与线性回归算法、标准BP神经网络算法,以及未分类的RPROP算法进行比较,验证了该方法的有效性。This paper proposed a line loss calculation based on the self-organizing competitive network of the RPROP neural network to find efficient loss reduction measures and provide the basis for scientifically formulating line loss targets.The RPROP neural network ensured that the network could converge under a limited number of training times. Moreover, it utilized a self-organizing competitive neural network to effectively classify informative data, which improved the output accuracy of the RPROP neural network.By doing simulation experiments on the MATLAB platform and comparing with linear regression algorithm, standard BP neural network algorithm, unclassified RPROP algo-rithm, it verified the effectiveness of the proposed method.

关 键 词：线性回归算法 BP神经网络 RPROP神经网络 自组织竞争神经网络 线损 

分 类 号：TM744[电气工程—电力系统及自动化]