ABSTRACT:
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 back propagation 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.
KEYWORDS:
1.
Artificial
neural network (ANN)
2.
Genetic
algorithm (GA)
3.
Modified
packed U-cell (MPUC) Inverter
4.
multilevel
inverter (MLI)
5.
selective
harmonic elimination (SHE)
6.
total harmonic
distortion (THD)
SOFTWARE: MATLAB/SIMULINK
CONCLUSION:
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