摘要
从 Hopfield神经网络的原理和机组组合问题的特点出发 ,提出了一种适合解决机组组合问题的扩展 Hopfield神经网络算法。该算法结合了 Hopfield神经网络和模拟退火算法 ( SA)的优点 ,对整数变量取值范围进行了可行的扩展 ,有效地避免了陷入局部最优。同时 ,该算法无须进行额外的负荷经济分配 ,故能迅速、高效地搜索到系统的高质量优化解。对于实际系统的仿真计算结果证明了该算法的有效性 ,并且 ,方法本身具有良好的并行性 ,易于在并行计算机上实现 。
Unit commitment is a very important issue of generation scheduling in electric power systems. A rational scheme will lead to cost savings. Based on the principle of Hopfield neural network and the characteristics of unit commitment, an improved Hopfield neural network algorithm is presented, which can be applied to the optimal generation unit commitment. The improved neural network combines good solution quality of simulated annealing with rapid convergence of Hopfield neural network. The method replaces the digital neurons with two binary states by analogue neurons with continuous output and dispenses with additional economic power dispatch, and can quickly search high-quality optimal solution of the system. The simulation results from a practical power system prove the method is very effective in reaching proper unit commitment. It has good parallel characteristics, and easily implemented on parallel computer. The possibility of the application to practical power systems is obvious.
出处
《电力系统自动化》
EI
CSCD
北大核心
2003年第7期41-44,共4页
Automation of Electric Power Systems
