Volume 10, Issue 2, October 2014, Pages 466–475
Shougat Nazbin Khan1 and M. Swaminathan2
1 M.Tech Green Energy Technology, Pondicherry University, Pondicherry, India
2 M.Tech Green Energy Technology, Pondicherry University, Pondicherry, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Modeling and Simulation of a solar based energy generation system is a significant tool in predicting the performance of both grid-connected and standalone PV systems. There are multiple ways by which a PV system can be connected to the grid. However, the output of PV systems varies due to change of solar irradiation, ambient temperature, wind speed and other climatic conditions, that makes the real field prediction becomes complicated. Hence we need better tools to predict the real field performance of PV power stations. Herein, we developed a MATLAB/Simulink model that can accommodate all field parameters to make a better prediction. The PV array model has been implemented through an embedded MATLAB model. The maximum power point tracking algorithm is implemented to DC/DC converter to operate PV arrays at maximum power point. The incremental conductance algorithm is employed to control the boost converter. The inverter controlled by the boost converter is interfaced with the grid. Finally, system performance is analyzed under various conditions. The system validation is also carrying out using the real power plant data. Simulations performed with field conditions at various locations. The model is helpful for optimizing the power output of a PV system as well as achieving cost effectiveness by incorporating different PV system types.
Author Keywords: PV on-grid, Standalone, Power prediction, MPPT, DC/DC Converter.
Shougat Nazbin Khan1 and M. Swaminathan2
1 M.Tech Green Energy Technology, Pondicherry University, Pondicherry, India
2 M.Tech Green Energy Technology, Pondicherry University, Pondicherry, India
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Modeling and Simulation of a solar based energy generation system is a significant tool in predicting the performance of both grid-connected and standalone PV systems. There are multiple ways by which a PV system can be connected to the grid. However, the output of PV systems varies due to change of solar irradiation, ambient temperature, wind speed and other climatic conditions, that makes the real field prediction becomes complicated. Hence we need better tools to predict the real field performance of PV power stations. Herein, we developed a MATLAB/Simulink model that can accommodate all field parameters to make a better prediction. The PV array model has been implemented through an embedded MATLAB model. The maximum power point tracking algorithm is implemented to DC/DC converter to operate PV arrays at maximum power point. The incremental conductance algorithm is employed to control the boost converter. The inverter controlled by the boost converter is interfaced with the grid. Finally, system performance is analyzed under various conditions. The system validation is also carrying out using the real power plant data. Simulations performed with field conditions at various locations. The model is helpful for optimizing the power output of a PV system as well as achieving cost effectiveness by incorporating different PV system types.
Author Keywords: PV on-grid, Standalone, Power prediction, MPPT, DC/DC Converter.
How to Cite this Article
Shougat Nazbin Khan and M. Swaminathan, “MODELLING AND SIMULATION OF ENERGY FORECASTER FOR GRID CONNECTED & STANDALONE PV SYSTEMS,” International Journal of Innovation and Scientific Research, vol. 10, no. 2, pp. 466–475, October 2014.
