An Artificial Neural Network-Based Comprehensive Solar Photovoltaic Emulator

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With increasing solar photovoltaic-based power generation, a photovoltaic emulator (PVE) is necessary to experimentally validate new control strategies without the influence of external factors such as irradiance and temperature. However, two significant challenges with PVEs are (i) solving the nonlinear equation of photovoltaic (PV) panel and (ii) oscillations in constant current (voltage) region with voltage (current) control. Thus, in this paper, a PVE with the ability to mimic both uniformly irradiated and partially shaded PV panels is proposed by employing artificial neural network (ANN) and piecewise-linearization technique. Based on the input operating conditions (irradiance, temperature, and partial shading), the ANN breaks the nonlinear I-V curve into piecewise-linear segments and outputs their boundary points. Then, with these boundary points, piecewise-linear equations of the segments relating PVE’s voltage and current are formed. Subsequently, using these piecewise-linear equations, the reference PVE voltage corresponding to PVE’s output current is calculated and given to the PI controller of a synchronous buck converter to mimic a PV panel. Thus, the proposed PVE overcomes the problem of solving nonlinear I-V equation by piecewise linearization which in turn aids an impedance-matching technique to mitigate the aforementioned oscillations. The generation of training data and development of ANN were carried out in MATLAB. Finally, the simulation studies performed in MATLAB/Simulink and hardware experiments validated the steady-state accuracy and the transient response which settled within 10 ms endorsing the real-time application of the proposed PVE.