引用本文:张潮海,周其节,毛宗源,朱德明.混合FLN－Lagrange松驰法用于机组最优投入[J].控制理论与应用,1996,13(6):811~816.[点击复制]
ZHANG Chaohai,ZHOU Qijie and Mao Zongyuan ,ZHU Deming.A Hybrid FLN and Lagrangian Relaxation Approach to Generator Unit Commitment*[J].Control Theory and Technology,1996,13(6):811~816.[点击复制]
混合FLN－Lagrange松驰法用于机组最优投入
A Hybrid FLN and Lagrangian Relaxation Approach to Generator Unit Commitment*
摘要点击 1056  全文点击 524  投稿时间：1994-11-16  修订日期：1996-04-15
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DOI编号  
  1996,13(6):811-816
中文关键词  机组优化组合  人工神经网络  Lagrange松弛法
英文关键词  unit commitment  artificial neural networks (ANN)  lagrangian relaxation method
基金项目  
作者单位
张潮海,周其节,毛宗源,朱德明 海军航空工程学院自控系 
中文摘要
      本文提出用混合函数链网络与Lagrange松驰法解机组最优投入问题．基于神经网络的监督学习和自适应模式识别概念，FLN被用来预测负荷需求与Lagrange乘子之间的关系．为了证实这一方法的有效性，一个具有16台电机组的实际系统被测试．数值计算结果表明系统发电总成本可获得最少，大大减少了计算时间．
英文摘要
      A hybrid method for achieving the generating unit commitment using a functional link network(FLN) is proposed in this paper. Based on the use of supervised learning neural-net technology and the adaptive pattern recognition concept，the developed FLN was used to presume the relationship between power demand pattern and Lagrange multipliers（LMPs）.To demonstrate the effectiveness of the proposed approach，a real power generation system with 16 thermal units was tested.Numerical results show that the system production cost was minimal and the time taken for processing the unit commitment scheduling in power systems was reduced.