遗传算法的RBF神经网络在线损计算中的应用  

Application of RBF Neural Network of Genetic Algorithm in Calculation of Line Losses

作　　者：张方舟[1] 郝庆辉 周勃[1] 刘庆[1] 韩东洋[1] 

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318

出　　处：《计算机技术与发展》2014年第6期192-195,199,共5页Computer Technology and Development

基　　金：国家电网公司科技项目(SG11012);黑龙江省科技计划项目(GZD9A120);黑龙江省教育科研项目(115510106)

摘　　要：电力网线损率是综合反映电力网规划设计、生产运行和经营管理的一项重要指标。如何有效地降低电力网电能损耗、提高企业经济效益已成为迫切需要解决的问题。文中提出一种基于遗传算法的RBF神经网络对线损进行计算,并且针对RBF神经网络的隐含层与输出层相互独立和输出结果容易陷入局部最小等缺点,运用遗传算法对整个网络进行优化,另外对遗传算法进行了相应的改进。为了验证算法的可行性,文中以大庆油田某一地区的67条线路为样本进行仿真计算。实验结果证明了遗传算法优化的RBF神经网络在计算精度和训练速度上都有较大提高。Lineloss rate is an important index reflecting the power network planning and design,producting operation and management.How to effectively reduce the power loss and improve the economic profit of enterprises have became an urgent problem.Present a RBF neural network based on genetic algorithm for line loss calculation and in view of the defects of RBF neural network hidden layer and output layer are independent of each other and the output is likely to fall into local minimum,apply genetic algorithm to optimize the whole network,in addition to the genetic algorithm for the corresponding improvements.In order to verify the feasibility of the algorithm,select67 line in a certain area of Daqing oil field as samples for simulation and experimental results indicate that the the RBF neural network optimized by genetic algorithm in the calculation precision and speed of train has increased greatly.

关 键 词：RBF神经网络 遗传算法 电网线损计算 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]