A New Piecewise Segmentation Based Solar Photovoltaic Emulator Using Artificial Neural Networks and a Nonlinear Backstepping Controller

The current state of affairs on the Photovoltaic emulator (PVE) is facing two main challenges: complexity in resolving the nonlinear equations of the photovoltaic (PV) and the problem of effective control of the PVE power conversion stage (PCS). In this paper, a new power electronics-based PVE is proposed to emulate the dynamic and static characteristics of the PV cell/module. The nonlinear equations of the PV cell/module are resolved using a new piecewise segmentation technique, involving the splitting of the current-voltage ( I–V ) curve into twelve linear segments associated with the letters a to m (a–m). Based on input environmental conditions, a trained artificial neural network (ANN) is constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. By the use of simple linear equations with the boundary coordinates, a reference voltage is then generated for the PVE. A nonlinear backstepping controller is designed to exploit the PVE reference voltage and stabilize the PCS. The stability of the controller is verified by Lyapunov laws. Optimal performance and control of the PCS were ensured by resorting to particle swarm optimization (PSO). The overall system has been investigated in the MATLAB environment with major tests including the response to fast-changing irradiance and temperature, the EN 50530 test, and the response to change in the load. The proposed PVE revealed a satisfactory dynamic performances in mimicking the PV characteristics. Furthermore, the accuracy of the PVE as a function of the mean absolute percentage error (MAPE) was found less than 0.5% even for the worst case of environmental conditions. Experimental validation of the proposed PVE under real environmental conditions further validated its good dynamic and static robustness.