Robust ANN-Based Control of Modified PUC-5 Inverter for Solar PV Applications

Conventional PI controllers are vulnerable to changes in parameters and are difficult to tune. In this work, an artificial neural network (ANN) based controller is developed for the robust operation of a single-phase modified packed U-cell five-level inverter (MPUC-5) for solar PV application under variable insolation conditions. An MPUC-5 is a converter with a main and an auxiliary dc link of equal magnitude; although five-level operation is also still feasible with different voltages also. The maximum power point (MPP) of a PV array changes with the variation in the solar insolation. This results in a variable voltage at the output of the boost converter while maintaining the load line at the MPP. Consequently, the fundamental value of the output of the MPUC-5 also tends to change. Thus, it is required to produce angles that commit to an ac output voltage with a constant fundamental value and constrained to a minimum total harmonic distortion along with a third-order harmonic mitigation as per the grid codes, irrespective of the change in the dc-link voltages. A genetic algorithm is employed for this purpose. A large dataset is prepared for two-angle and four-angle operation of MPUC-5 under various dc-link voltages and constraints with which an ANN-based controller is trained. A neural network with a hidden layer is trained with the backpropagation technique; and once a correlation is developed, the network can be operated for a wide range of operating conditions. The robustness of the controller is verified through simulation in MATLAB/Simulink environment and validated by experimental emulation in an hardware in loop environment.