针对含有随机噪声的模型未知线性时不变(LTI,linear time invariant)系统模型建立过程复杂且控制律难以得到的问题,提出一种基于数据驱动的预测控制方法;基于系统行为学理论和平衡子系统辨识方法,仅利用测量得到的系统数据构建被控系统...针对含有随机噪声的模型未知线性时不变(LTI,linear time invariant)系统模型建立过程复杂且控制律难以得到的问题,提出一种基于数据驱动的预测控制方法;基于系统行为学理论和平衡子系统辨识方法,仅利用测量得到的系统数据构建被控系统的非参数模型,将其和预测控制理论相结合设计出基于数据驱动的预测控制器,对于系统测量数据中存在的有界加性高斯噪声,通过引入数据的松弛变量和L2正则项来降低噪声扰动的影响,采用滚动时域优化策略计算最优控制序列并将其作用于被控系统,实现了系统对设定值的轨迹跟踪;将所提控制策略应用于四容水箱系统,仿真结果表明所提方法能实现四容水箱系统的液位跟踪控制,且与同样基于数据驱动的子空间预测控制方案相比,所提方法具有更好的动态性能,且该策略在抗噪声扰动方面有明显优势,具有更强的鲁棒性。展开更多
This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbit...This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbitrage or peak load reduction. The methods compared include: a neural network(NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network(WNN)model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system(ANFIS) approach.The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge(SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic(PV)field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.展开更多
文摘针对含有随机噪声的模型未知线性时不变(LTI,linear time invariant)系统模型建立过程复杂且控制律难以得到的问题,提出一种基于数据驱动的预测控制方法;基于系统行为学理论和平衡子系统辨识方法,仅利用测量得到的系统数据构建被控系统的非参数模型,将其和预测控制理论相结合设计出基于数据驱动的预测控制器,对于系统测量数据中存在的有界加性高斯噪声,通过引入数据的松弛变量和L2正则项来降低噪声扰动的影响,采用滚动时域优化策略计算最优控制序列并将其作用于被控系统,实现了系统对设定值的轨迹跟踪;将所提控制策略应用于四容水箱系统,仿真结果表明所提方法能实现四容水箱系统的液位跟踪控制,且与同样基于数据驱动的子空间预测控制方案相比,所提方法具有更好的动态性能,且该策略在抗噪声扰动方面有明显优势,具有更强的鲁棒性。
文摘This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbitrage or peak load reduction. The methods compared include: a neural network(NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network(WNN)model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system(ANFIS) approach.The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge(SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic(PV)field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.
