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引用次数: 4
摘要
神经网络模型预测控制(Neural Network Model Predictive Control, NNMPC)与模型预测控制非常相似,但所使用的机载目标是基于人工神经网络的概念来预测目标的行为。为了获得更好的控制变量,将预测值输入优化器。这种类型的控制器将用于取代最通用的FACTS设备中的传统控制器,即统一潮流控制器(UPFC)。UPFC具有控制输电线路参数的能力,从而控制输电线路中有功功率和无功功率的流动。因此,这种基于人工神经网络(ANN)概念的自适应控制器将在UPFC中实现,并将对其进行研究,以确保其鲁棒性,有效性以及适应单机到无限总线(SMIB)系统中任何突然负载变化的能力。此外,将NNMPC的动态性能与另一种称为模型生产控制器(MPC)的自适应控制器方案进行比较。
Neural Network Model Predictive Control (NNMPC) Design for UPFC
Neural Network Model Predictive Control (NNMPC) is like almost like the model predictive control but the used inboard plant is designed based on using the concept of the artificial neural network to predict the behavior of the plant. The predicted values are fed to the optimizer in order to obtain better control variables. This type of controller will be used instead of the conventional controller in the most versatile FACTS devices, which is the Unified Power Flow Controller (UPFC). UPFC has the capability of controlling the transmission line parameters and consequently the flow of the active and reactive power in the transmission line. So, this type of adaptive controller, which is based on Artificial Neural Network (ANN) concept, will be implemented in UPFC, and will be investigated to ensure its robustness, effectiveness and the capability to accommodate any sudden load change in the system of Single Machine to Infinite Bus (SMIB). In addition, the dynamic performance of NNMPC will be compared with another type of adaptive controller scheme called Model Productive Controller (MPC).