摘要
本文构造了基于分布估计算法(Estimation of Distribution Algorithm,EDA)和遗传算法(GeneticAlgorithm,GA)融合的神经网络(Neural Network,NN)故障诊断模型。传统的GA看作是对生物进化"微观"层面上的模拟,则EDA是对生物进化"宏观"层面上的建模,是一种全新的进化模式。EDA与GA融合的实质是在解空间"宏观"和"微观"两个层面进行寻优,可克服NN陷入局部最小,提高NN的泛化能力,使故障诊断的容错性能得到有效改善。将该模型用于高压输电线系统的故障诊断,并作容错性能的评估。由仿真测试表明,研究模型的容错性能要优于传统的BP-NN模型和单纯GA优化NN模型。因此,新诊断模型是有一定的理论和实用价值的。
A fault diagnosis model using NN （neural network） based on EDA （estimation of distribution algorithm） combined with GA （Genetic Algorithm） is constructed in this paper. The traditional GA is regarded as the simulation of biological evolution from microscopic level, while EDA is from macroscopic level. EDA is a kind of novel evolution mode. The essence of combining EDA with GA is to search the optimal solution from microscopic and macroscopic level, meanwhile to avoid NN to immerse in the local minimal points and improve generalization ability, so fault-tolerance performance of fault diagnosis model can be effectively improved. The presented model is used as the fault diagnosis in high voltage transmission line system, and their fault-tolerance performance is assessed. Through the simulation and test, it shows that the fault-tolerance performance of researched model is superior to that of the diagnosis model corresponding BP-NN and GA-NN. So researched diagnosis model possess theoretical and practical value.
出处
《电工电能新技术》
CSCD
北大核心
2008年第3期18-21,48,共5页
Advanced Technology of Electrical Engineering and Energy
基金
中国电力联合会许继科技计划项目
青岛大学引进人才科研基金项目
关键词
高压输电系统
故障诊断
容错性能
分布估计算法
遗传算法
神经网络
high voltage transmission line system （HVTLS）
fault diagnosis （FD）
fanlt-tolerance performance
estimation of distribution algorithm （EDA）
genetic algorithm （GA）
neural network （NN）
