火电厂AGC控制回路的非线性分析
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摘要
火电厂自动发电控制AGC是保障发供电频率和功率平衡的关键。正如非线性是任何系统或多或少都存在的特性一样,AGC控制回路中往往具有大延迟、大惯性环节等非线性特性。本文通过自回归各态历经ARX模型和人工智能领域的BP神经网络,分析AGC控制回路的非线性特性。结果显示:BP神经网络在分析AGC控制回路时,输出拟合度高、速度快,表明AGC控制回路具有明显的非线性特性,为AGC控制回路制定控制方案或进行系统辨识提供参考。
Automatic generation control(AGC) is the key to ensure the balance of generated power and supply power in thermal power plants. Just as every system possessed nonlinear performance more or less, AGC control loop always have large delay and large inertia nonlinear performance. In this paper, we analysis the nonlinear of AGC control loop with two tools. The first tool is autoregressive ergodic(ARX) model, the second tool is BP neural network. The result is the output fitting degree with BP neural network is higher than ARX, the velocity with BP neural network is faster than ARX. So BP neural network can analysis the nonlinear better than ARX for AGC control loop in power plant. This show that the AGC control loop have obvious nonlinear performance. This can help how to control and identify the AGC control loop.
Automatic generation control(AGC) is the key to ensure the balance of generated power and supply power in thermal power plants. Just as every system possessed nonlinear performance more or less, AGC control loop always have large delay and large inertia nonlinear performance. In this paper, we analysis the nonlinear of AGC control loop with two tools. The first tool is autoregressive ergodic(ARX) model, the second tool is BP neural network. The result is the output fitting degree with BP neural network is higher than ARX, the velocity with BP neural network is faster than ARX. So BP neural network can analysis the nonlinear better than ARX for AGC control loop in power plant. This show that the AGC control loop have obvious nonlinear performance. This can help how to control and identify the AGC control loop.
引文
[1]刘维烈.电力系统调频与自动发电控制[M].北京:中国电力出版社,2006.
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[3]谭涛,姜杰,何潜,等.省级电网AGC控制策略研究[J].电力系统保护与控制,2011,39(16):63-66.
[4]颜伟,赵瑞锋,赵霞,等.自动发电控制中控制策略的研究发展综述[J].电力系统保护与控制,2013(08):149-155.
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[12]杨剑锋,钱积新,赵均.基于稳态非线性模型和线性ARX模型组合的非线性预测控制[J].信息与控制,2008(02):219-223,234.
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[19]李丽荣,韩璞,董泽,等.人工神经网络在系统辨识中的研究与应用[J].华北电力大学学报,2000(03):28-33.
[20]陈祥光,黄聪明.神经网络智能控制系统辨识模型结构的研究[J].北京理工大学学报,1999(01):57-61.
[2]Kothari D P,Nagrath I J.Modern Power system analysis[M].Tata McGraw-Hill Education,2003.
[3]谭涛,姜杰,何潜,等.省级电网AGC控制策略研究[J].电力系统保护与控制,2011,39(16):63-66.
[4]颜伟,赵瑞锋,赵霞,等.自动发电控制中控制策略的研究发展综述[J].电力系统保护与控制,2013(08):149-155.
[5]赵祥生,等.热力过程自动化[M].北京:中国电力出版社,1996.
[6]孙吉星,冯雅琴同.热工测量及显示仪表[M].北京:水利电力出版社,1998.
[7]夏长天,熊光楞,李芳芸.系统辨识:最小二乘法[M].北京:清华大学出版社,1983.
[8]沈善德.电力系统辨识[M].北京:清华大学出版社,1993.
[9]张晓文,杨煜普,许晓鸣.神经网络传递函数的功能分析与仿真研究[J].计算机仿真,2005(10):176-178.
[10]胡寿松.自动控制原理:第四版[M].北京:科学出版社,2001.
[11]华晨,李柠,李少远.分布参数系统的时空ARX建模及预测控制[J].控制理论与应用,2011(12):1711-1716.
[12]杨剑锋,钱积新,赵均.基于稳态非线性模型和线性ARX模型组合的非线性预测控制[J].信息与控制,2008(02):219-223,234.
[13]刘洪伟.线性模型最小二乘估计递推算法[D].哈尔滨:东北林业大学,2012.
[14]D.E.Rumelhart,G.E.Hinton and R.J.Williams.Learning Interna1 Representations by Error ProPagation in Parallel Distributed Proeessing.Vol.1,D.E.Rumelhart and J.L.McLelland,Eds.Cambridge,MA:MIT Press,1986:318-362.
[15]孙增沂,邓志东,张再兴,等.智能控制理论与技术[M].北京:清华大学出版社,1997.
[16]李阳春.自动控制理论在火电厂热工自动化中的应用[D].杭州:浙江大学,2001.
[17]华志刚.先进控制方法在电厂热工过程控制中的研究与应用[D].南京:东南大学,2006.
[18]熊沈蜀,周兆英.单层神经网络在参数估计中的应用[J].清华大学学报:自然科学版,1996(04):1-6.
[19]李丽荣,韩璞,董泽,等.人工神经网络在系统辨识中的研究与应用[J].华北电力大学学报,2000(03):28-33.
[20]陈祥光,黄聪明.神经网络智能控制系统辨识模型结构的研究[J].北京理工大学学报,1999(01):57-61.