ABSTRACT:
Residential solar photovoltaic (PV) energy is becoming an increasingly important part of the world's renewable energy. A residential solar PV array is usually connected to the distribution grid through a single-phase inverter. Control of the single-phase PV system should maximize the power output from the PV array while ensuring overall system performance, safety, reliability, and controllability for interface with the electricity grid. This paper has two main objectives. The first objective is to develop an artificial neural network (ANN) vector control strategy for a LCL-filter based single-phase solar inverter. The ANN controller is trained to implement optimal control, based on approximate dynamic programming. The second objective is to evaluate the performance of the ANN-based solar PV system by (a) simulating the PV system behavior for grid integration and maximum power extraction from solar PV array in a realistic residential PV application and (b) building an experimental solar PV system for hardware validation. The results demonstrate that a residential PV system using the ANN control outperforms the PV system using the conventional standard vector control method and proportional resonant control method in both simulation and hardware implementation. This is also true in the presence of noise, disturbance, distortion, and non-ideal conditions.
KEYWORDS:
1. Artificial neural networks
2. DC-AC power converters
3. DC-DC power
converters
4. Dynamic programming
5. Maximum power point tracker
6. Optimal control
7. Solar power generation
SOFTWARE: MATLAB/SIMULINK
CONCLUSION:
This paper proposes a single-phase,
residential solar PV system based on artificial neural networks and adaptive dynamic
programming for MPPT control and grid integration of a solar photovoltaic array
through an LCL-filter based inverter. The proposed artificial neural network
controller implements the optimal control based on the approximate dynamic
programming. Both the simulation and hardware experiment results demonstrate
that the solar PV system using the ADP-based artificial neural network
controller has more improved performance than that using the proportional resonant
or conventional standard vector control techniques, such as no requirement for
damping resistance, more reliable and efficient extraction of solar power, more
stable DC-link voltage, and more reliable integration with the utility grid. Using
the ADP-based neural network control technique, the harmonics are significantly
reduced and the system shows much stronger adaptive ability under uncertain
conditions, which would greatly benefit the integration of small-scale residential
solar photovoltaic systems into the grid.
REFERENCES:
[1] Renewable Energy World Editors.
(2014, Nov. 12). Residential Solar Energy Storage Market Could Approach 1 GW by
2018. Available: http://www.renewableenergyworld.com.
[2] R. A. Mastromauro, M. Liserre and A.
D. Aquila, “Control Issues in Single-Stage Photovoltaic Systems: MPPT, Current
and Voltage Control”, IEEE Trans. Ind. Informatics, vol. 8, no. 2, pp.
241-254, May 2012.
[3] E. Lorenzo, G. Araujo, A. Cuevas, M.
Egido, J. Miñano and R. Zilles, Solar Electricity: Engineering of
Photovoltaic Systems, Progensa, Sevilla, Spain, 1994.
[4] J. M. Carrasco, L. G. Franquelo, J.
T. Bialasiewicz, E. Galván, R. C. P. Guisado, M. Á. M. Prats, J. I. León, and
N. Moreno-Alfonso, “Power- Electronic Systems for the Grid Integration of
Renewable Energy Sources: A Survey”, IEEE Trans. Ind. Electron., vol.
53, no. 4, pp. 1002-1016, August 2006.
[5] W. T. Franke, C. Kürtz and F. W.
Fuchs, "Analysis of control strategies for a 3 phase 4 wire topology for
transformerless solar inverters," in Proc. IEEE Int. Symp. Ind.
Electron., Bari, pp. 658-663, 2010.
