الملخص الإنجليزي
bstract: The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity,
frequency and transients sanity. The varying generation from renewable resources, fluctuating loads,
and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus,
frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control
of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great
importance for the quality/smooth operation of power flow, power sharing and stability of the whole
power system. In literature, various control schemes are suggested, like conventional droop control,
communication-based control, model predictive control, etc., each addressing different stability issues
of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the
authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive
neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The
proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction,
(iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three
novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms
are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes
are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive
full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive
NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network
Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent
Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control
are modeled and tested for the control of BIC. The controllers differ from each other, based on variants
used in the antecedent part (Gaussian membership function and B-Spline membership function), and
consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent
adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for
various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The
simulation results were bench-marked against an aPID controller, and each proposed control scheme,
for a simulation time of a complete solar day
