摘要
为了解决负荷模型参数辨识结果平稳性这一困扰模型应用的难题,提出了一种适用于负荷模型参数辨识的混沌蚁群混合算法。该算法针对蚁群算法容易陷入局部最优的缺点和混沌算法遍历性和随机性的优点,把混沌算法引入到了蚁群算法中,在蚁群算法求解的基础上,利用混沌算法对解的邻域进行了混沌优化,有效避免了蚁群算法的局部收敛问题。基于实测数据的算例结果表明:与单一蚁群算法相比,混沌蚁群混合算法提高了辨识结果的精度,减少了辨识误差,有效控制了参数分散性,具有较好的工程实用价值。
In order to solve the steadiness of model parameters identification result,which is an obstacle in the application of the load model,a new algorithm of the chaos ant colony optimization is proposed for the load model parameter identification.Contraposing to the ant colony algorithm easy to fall into local optimum and the chaos＇s ergodicity and randomness,t he chaos algorithm is introduced into the ant colony algorithm,o ptimizing the neighborhoods of solutions based on the chaotic algorithm,a voiding the local convergence of ant colony algorithm effectively.The simulation results based on measured data show that,compared with the single ant colony algorithm,t he chaos ant colony optimization algorithm improves the precision of identification results,r educes the identification error,o vercomes the dispersiveness of model parameters,a nd has a good practical value.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2011年第14期47-51,共5页
Power System Protection and Control
基金
四川省应用基础研究项目(07JY029-063)
关键词
负荷模型
参数辨识
蚁群算法
混沌
优化
load model
parameter identification
ant colony algorithm
chaos
optimization
