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多边界条件下热泵利用循环水余热的CPCS-RBF预测控制

DOI: 10.13334/j.0258-8013.pcsee.2015.03.018, PP. 645-651

Keywords: 循环水余热,直接多步预测控制,混沌变异克隆选择,驱动蒸汽,径向基函数(RBF)神经网络

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Abstract:

循环水余热回收系统中,热泵热网水出口温度在跟踪供热负荷需求时,在受驱动蒸汽量的调节的同时,往往易受热网回水、循环水等工况变化的影响,传统PID控制方式超调量大、负荷跟踪能力差。提出一种混沌变异克隆选择-径向基函数(CPCS-RBF)直接多步预测控制策略,以热泵热网水出口温度预测值与设定值差值为目标函数,利用CPCS优化算法求取目标函数最小时的驱动蒸汽最佳值。预测模型由2个RBF神经网络结合热泵现场运行数据构建,以提高热泵系统适应工况变化的能力;实验结果表明,该控制策略能综合学习热网回水温度、循环水温度等参数的变化,使驱动蒸汽调门超前动作,及时跟踪供热负荷需求变化的同时,适应发电负荷变化下排气余热量的波动,具有更好的节能效果和变工况适应能力。

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