基于RBF神经网络单神经元PID控制在低压直流变换中的应用研究
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摘要
太阳能和风能发电系统的非线性特征非常明显,并且二次端直流负载的接入与断开的随机性也比较大,容易引起微型电网的波动。为了解决低压直流微型电网的波动特性,提出了基于RBF神经网络在线辨识的单神经元PID控制的直流变换方法,该方法结合了普通PID控制结构简单和神经网络非线性系统自适应性强的特性,解决了二次直流负载端稳定的直流电源供电问题。
With the widespread application of clean solar energy and wind energy, the island-style micro DC power grids are used in remote areas and isolated islands. In these places, the irregular characteristics of the atmosphere often result in the obvious nonlinear characteristics of the solar and wind power generation system, and the large randomness of the access and disconnection of the secondary-side DC load, which is easy to cause the fluctuations of the micro grid. In order not to slove the fluctuation charateristics of low voltage Dcmicro-grid. the single-neuron PID control DC conversion based on RBF neural network online identification is proposed in this paper combined with the advantages of both the simple structure of PID control and the strong adaptability of neural network to nonlinear systems, conducive to solving the problem of secondary DC load stability.
With the widespread application of clean solar energy and wind energy, the island-style micro DC power grids are used in remote areas and isolated islands. In these places, the irregular characteristics of the atmosphere often result in the obvious nonlinear characteristics of the solar and wind power generation system, and the large randomness of the access and disconnection of the secondary-side DC load, which is easy to cause the fluctuations of the micro grid. In order not to slove the fluctuation charateristics of low voltage Dcmicro-grid. the single-neuron PID control DC conversion based on RBF neural network online identification is proposed in this paper combined with the advantages of both the simple structure of PID control and the strong adaptability of neural network to nonlinear systems, conducive to solving the problem of secondary DC load stability.
引文
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[7] 沈赋,尹斌,孙维广.基于单神经元自适应PID的光伏发电MPPT[J].电力系统及其自动化学报,2017,29(2):89-95.
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[2] 王琛,孟建辉,王毅,等.考虑蓄电池荷电状态的孤岛直流微网多源协调控制策略[J].高电压技术,2018,44(1):160-168.
[3] 杨向宇,蔡晓铭,姚佳.基于模糊PI控制的永磁同步电动机伺服控制器设计[J].华南理工大学学报(自然科学版),2004,32(11) :15-18.
[4] 夏长亮,王明超.基于RBF神经网络的开关磁阻电机单神经元PID控制[J].中国电机工程学报,2005,25(15):161-165.
[5] 马青云,黄凌,宋亚君,等.基于RBF神经网络的中压配电网接地故障特性研究[J].电力与能源,2019,40(1):20-24.
[6] 贺虎成,胡春龙,王勉华,等.基于单神经元自适应PID的SRM直接转矩控制[J].微电机,2015,48(9):71-74.
[7] 沈赋,尹斌,孙维广.基于单神经元自适应PID的光伏发电MPPT[J].电力系统及其自动化学报,2017,29(2):89-95.
[8] 余致廷,秦亚胜,杨婷,等.基于模型参考自适应的单神经元PID控制器设计[J].电力电子技术,2016,50(5):89-92.