摘要
根据溪洛渡水库的具体情况,建立了以发电量最大为目标的水库优化调度非线性数学模型,并利用遗传模拟退火算法(GSA)和小生境遗传算法(NGA)分别求解模型。结果表明,GSA和NGA的收敛速度和计算结果都明显优于基本遗传算法;且两者相比,GSA的收敛性更强,但计算时间较长。而在求解水库长系列优化调度问题时,各遗传算法占用机时太多,且收敛能力较差。
Based on the characteristics of the Xiluodu Reservoir, a nonlinear reservoir optimal operation model guided by maximum generation is developed. The model is separately solved by genetic simulated annealing algorithm （GSA） and niche genetic algorithm （NGA）. The results show that the convergency of GSA and NGA is better than that of simple genetic algorithm （SGA）, and the convergency of GSA is better than that of NGA, but with longer calculation time. For large-scare long-term optimal operation, the calculation time and convergency of all genetic algorithms are not satisfactory.
出处
《水电自动化与大坝监测》
2008年第4期1-4,共4页
HYDROPOWER AUTOMATION AND DAM MONITORING
基金
国家自然科学基金资助项目(50779049)
关键词
水库优化调度
遗传算法
遗传模拟退火算法
小生境遗传算法
收敛性
reservoir optimal operation
genetic algorithm （GA）
genetic simulated annealing algorithm （GSA）
niche genetic algorithm （NGA）
convergency
