This paper mainly aims at the modeling problem of the photovoltaic (PV) array with a 30 kW PV grid-connected generation system. An iterative method for the time-varying parameters is proposed to model a plant of PV array. The relationship of PV cell and PV array is obtained and the solution for PV array model is unique. The PV grid-connected generation system is used to demonstrate the effectiveness of the proposed method by comparing the calculated values with the actual output of the system.
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
In this paper, the online correction model predictive control （MPC） strategy is presented for partial dif- ferential equation （PDE） unknown spatially-distributed systems （SDSs）. The low-dimensional MIMO models are obtained using principal component analysis （PCA） method from the high-dimensional spatio-temporal data. Though the linear low- dimensional model is easy for control design, it is a linear approximation for nonlinear SDSs. Thus, the MPC strategy is proposed based on the online correction low-dimensional models, where the state at a previous time is used to correct the output of low-dimensional models and the spatial output is correct by the average deviation of the historical data. The simulations demonstrated show the accuracy and efficiency of the proposed methodologies.
