Artificial Neural Network Based Power Flow Analysis for Micro Grids

This paper proposes a neural network based power flow analysis method that applied on a grid connected and ring-shaped micro grid. As the use of micro grids increasing rapidly, it becomes necessary to analyze them for different operating and loading conditions as large power systems. At the outset, a MG is designed and simulated under MATLAB / Simulink platform. Normal operation data collected and stored. Then, different loading scenarios performed, operational data collected and stored to use for proposed method. Intelligent systems are used to process these data and also for training. After training a fully different scenario is created and the effectiveness of the proposed method is verified through simulation study

Keywords:

Artificial neural networks, micro grid power flow analysis, renewable energy sources,

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Z. Zhang, X. Huang, J. Jiang and B. Wu, “A load-sharing control scheme for a micro grid with fixed frequency inverter”. Electric Power Systems Research, vol. 80, pp. 311-317, 2010.
A. Elrayyah, Y. Sozer, and M. E. Elbuluk, “A Novel Load-Flow Analysis for Stable and Optimized Microgrid Operation”, IEEE Trans. on Power Delivery, vol. 29, no. 4, pp. 1709-1717, Aug. 2014.
W.Q. Sun, K. Xia1, H.Y. Li, C.M. Wang, “Power System Node Loadability Evaluation Using Flexibility Analysis Method”, ELEKTRONIKA IR ELEKTROTECHNIKA, vol. 20, no. 6, pp. 51-56, 2014.
M. Brenna, F. Foiadelli and D. Zaninelli, “The impact of the wind generation connected to weak grids”, International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Ischia, Italy, June 11-13, 2008.
B. Singh and G. K. Kasal, “Voltage and Frequency Controller for a Three-Phase Four-Wire Autonomous Wind Energy Conversion System”, IEEE Trans. on Energy Conversion, vol. 23, no. 2, pp. 509-518, June 2008.
X. Li, “Principles of Fuel Cells” in Mechanical Engineering Series, Taylor & Francis, New York, 2006, pp.21-22.
L. Powell, “Power System Load Flow Analysis”, 1st ed., McGraw-Hill, 2004, pp.15-16.
H. Shi, Y. Gao and X.Wang, “Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method”, Int J Adv Manuf Technol, vol. 48, pp.955-962, 2010.