https://orcid.org/0000-0003-0703-1371
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ABSTRACT
The fast advancement of photovoltaic (PV) power generation technologies led to the integration of solar-based generation systems into several modes of transportation, including aircraft, automobiles, and trains. In these PV systems, solar irradiation levels fluctuate significantly and invalidate specific maximum power point (MPP) tracking (MPPT) control strategies, lowering the energy conversion efficiency. It is evident that the new robust model reference adaptive control (MRAC) proposed in this paper alleviates these problems by reducing tracking direction loss and oscillations near MPP. To evaluate the proposed controller’s performance, MATLAB/Simulink is utilized and compared with well-known techniques (P&O, VSPO, INC, ANFIS, and swarm-based MPPT) under three different modes, i.e. stand-alone, grid-connected, and real-time mode. The proposed scheme has tracking efficacy lies between 99.02% and 99.96% and achieved MPP in just 4 msec under highly fluctuating radiation and temperature conditions. It takes only 0.08 sec to capture the global MPP, which is on average two times faster than swarm-based global MPPT methods. Furthermore, the effectiveness is tested in a 50 kW three-phase grid-connected mode with and without cloud effects in realistic weather situations. Finally, real-time validation on the OPAL-RT simulator demonstrates the proposed technique’s practicality in real-world applications.
Nomenclature
| Abbreviations | = | |
| PV | = | Photovoltaic |
| MPPT | = | Maximum power point tracking |
| MPP | = | Maximum power point |
| MRAC | = | Model reference adaptive control |
| THD | = | Total harmonic distortion |
| PSC | = | Partial shading conditions |
| GMPP | = | Global maximum power point |
| LMPP | = | Local maximum power point |
| INC | = | Incremental conductance |
| P&O | = | Perturb & observe |
| VSPO | = | Variable step perturb & observe |
| GA | = | Genetic algorithm |
| PSO | = | Particle swarm optimization |
| FLC | = | Fuzzy logic control |
| ANN | = | Artificial neural network |
| SOFT | = | Steady output and fast-tracking |
| ANFIS | = | Adaptive neuro-fuzzy inference system |
| ARFPI | = | Adaptive robust fuzzy proportional-integral |
| ABC | = | Artificial bee colony |
| BAT | = | Bat optimization algorithm |
| CSA | = | Cuckoo search algorithm |
| GWO | = | Gray wolf optimization |
| SSA | = | Slap swarm optimization |
| WOA | = | Whale optimization algorithm |
| MFO | = | Moth flame optimization |
| FFO-GRNN | = | Fruit fly optimization-general regression neural network |
| SMC | = | Sliding mode controller |
| ISSA | = | Improved squirrel search algorithm |
| MBO | = | Modified Butterfly Optimization |
| OIC | = | optimal integrator controller |
| SISO | = | Single input single output |
| HWO-PS | = | hybrid whale optimization-pattern search |
| DOA | = | dragonfly optimization algorithm |
| RMSE | = | Root mean square error |
| MRE | = | Mean relative error |
| MAPE | = | Mean absolute percentage error |
| ipv | = | PV array output current (A) |
| vpv | = | PV array output voltage (V) |
| p | = | PV array power (W) |
| ID | = | Reverse saturation current (A) |
| IL | = | Photocurrent (A) |
| Voc | = | Open circuit voltage (V) |
| Isr | = | Short circuit current (A) |
| Tcref | = | Cell reference temperature (K) |
| Tc | = | Cell temperature (K) |
| Ksc | = | Temperature coefficient of Isr (A/K) |
| k | = | Boltzmann constant, k = 1.38 × 10−23(J/K) |
| Eg | = | Bandgap energy (eV) |
| G | = | Solar irradiation |
| Ai | = | Ideality factor |
| q | = | Charge of an electron, q = 1.6 × 10−19 C |
| Rse | = | Series resistance |
| Rpe | = | Parallel resistance |
| îpv | = | Small-signal array current |
| = | Small-signal array voltage | |
| = | Small-signal duty cycle | |
| V0 | = | Boost converter steady-state output |
| vref | = | Reference voltage |
| CDR | = | Critically damped response |
| e | = | Error |
| up(t) | = | Plant Input |
| yp(t) | = | Plant output |
| ym(t) | = | Reference model output |
| r(t) | = | Reference model input |
| ap,bp,kp | = | Plant model parameters |
| am,bm,km | = | Reference model parameters |
| Gp(s) | = | Plant transfer function |
| Gm(s) | = | Reference model transfer function |
| γ | = | Adaptation gain |
| PMPP | = | Rated power |
| IMPP | = | Rated current |
| VMPP | = | Rated voltage |
| Ci | = | Boost input capacitor |
| L01 | = | Boost inductor |
| C0 | = | Boost capacitor |
| Ri | = | Solar array resistance |
| R0 | = | Load resistance |
| VIN | = | Input voltage range |
| fs | = | Switching frequency |
| Ts | = | Simulation step time |
Disclosure statement
There are no conflicts of interest, according to the authors.
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