Analysis of Line Instability in Microgrid by Applying Electrical Power Forecasting Approach

Authors

  • Richard Joseph Mushi School of Electrical Engineering, Suranaree University of Technology
  • Terapong Boonraksa School of Electrical Engineering, Suranaree University of Technology
  • Ashok Paudel School of Electrical Engineering, Suranaree University of Technology
  • Boonruang Marungsri School of Electrical Engineering, Suranaree University of Technology

Keywords:

Electric Power Forecasting, Microgrid, Power Flow, Line Stability

Abstract

The line instability and electrical power forecasting are essential techniques for operation control and planning in the microgrid. This paper presents an analysis of the line instability in microgrid by applying the electrical power forecasting approach. A mathematical model of power forecasting was examined using the modified GM (1, 1) model. Besides that, the power flow was represented using a modified IEEE 30 bus test system in MATLAB. Finally, the line instability was analyzed based on the results of power flow, using line stability factor (LQP). The results exhibited that the actual power was close to the power forecasting when the modified GM (1, 1) model combined together with exponential smoothing method. There was a high accuracy of prediction with Mean Absolute Percentage Error (MAPE) less than 1 %. Apart from that, it was cleared shown that the most critical line was 28-27, with a line instability factor of 0.963.  The second and third most vital lines were 25-26 and 2-4, having instability factors of 0.759 and 0.568, respectively. For a next day operation and planning in the microgrid, care must be taken by the utility operator to make sure that the microgrid is managed successfully.

References

Joseph, R. & Marungsri, B. (2019, September 23-24). Evaluation of line voltage stability index in micro-grid with or without BESS. PEA Conference and Innovation, Bangkok, Thailand.

Subramani, C., Subhransu, S. D., Bhaskar, A. M. & Jagdeshkumar, M. (2009). Simulation technique for voltage stability analysis and contingency ranking in power systems. International Journal of Recent Trends in Engineering, 2(5), 263-267.

Thasnas, N & Siritaratiwat, A. (2019). Implementation of static line voltage stability indices for improved static voltage stability margin. Journal of Electrical and Computer Engineering, from https://doi.org/10.1155/2019/2609235

Goh, H. H., Chua, S. S., Lee, W. S., Kok, C. B., Goh, C. K. & Teo, K. T. K. (2015). Evaluation for voltage stability indices in power system using artificial neural network. Procedia Engineering, 118, 1127-1136.

Lotufo, P. D. A & Minussi, R. C. (2002, August 28 – September 2). Electric power system load forecasting: A survey. IEEE - International Conference on Power Technology, Budapest, Hungary.

Piao, M., Lee, G. H, Park, H. J. & Ryu, H. K. (2008, September 15-18). Application of classification methods for forecasting mid-term power load patterns. 4th International Conference on Intelligent Computing, Shanghai, China.

Deng, J. (1989). Introduction to Grey System Theory. Journal of Grey System, 1, 1-24.

Huang, Y., Fang, W., & Fan, Z. (2011, January 8-9). Application research of improved Grey Forecasting Model in load forecasting, International Conference on Intelligent Computing and Information Science, Chongqing, China.

Wang, H. C & Phan, T. V. (2014). An improvement of the accuracy of Grey Forecasting Model for cargo throughput in international commercial ports of Kaohsiung. WSEAS Transactions on Business and Economics,11, 322-327.

Kayacan, E., Ulutas, B. & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37, 1784-1789.

Oukennou, A. and Sandali, A. (2019). An improved reactive line index for line ranking and voltage collapse prediction in transmission power system. Journal of Electrical Systems, 15(3), 480 – 498.

Reis, C., Andrade, A. & Maciel, F. P. (2009, March 18 – 20). Line Stability Indices for Voltage Collapse Prediction. International Conference on Power Engineering, Energy, and Electrical Drives, Lisbon, Portugal.

Choudekar, P., Asija, D. & Ruchira, (2017). Prediction of voltage collapse in power system using voltage stability indices. Proceeding of International Conference on Intelligent Communication, Control and Devices, Dehradun, India.

Hsu, C. & Chen, C. (2003). Application of improved Grey Prediction Model for power demand forecasting. Energy Conversion and Management, 44(12), 2241-2249.

Wang, Y., Liu, Q., Tang, J., Cao, W. & Li, X. (2014). Optimization approach of background value and initial item for improving prediction precision of GM (1, 1) model. Journal of Systems Engineering and Electronics, 25(1), 77-82.

Sobhani, M., Campbell, A., Sangamwar, S., Li, C. & Hong, T. (2019). Combine weather stations for electric load forecasting, Energies, 12(8), 1-11.

Neon Neue Energieökonomik GmbH. (n.d.). Data Platform. Retrieved August 20, 2019, from https://data.open-power-system-data.org/time_series/

Eminoglu, U. & Hocaoglu, M. H. (2007, September 4 - 6). A voltage stability index for radial distribution networks. 42nd International Universities Power Engineering Conference, Brighton, UK

Mohamed, A & Jasmon. G. B. (2007). A new clustering technique for power system stability analysis. Journal of Electrical Machines and Power Systems, 23(4), 389-403.

Moghavemmi, M. & Omar, F. M. (1998). A technique for contingency monitoring and voltage collapse prediction. IET - Proceedings Generation Transmission and Distribution, 145(6), 634-640.

Musirin, I & Abdul Rahman, T. K. (2002). Estimating maximum load ability for weak bus identification using FVSI. Power Engineering Review, 12(11), 50-52.

Zhang, X., & Wong, C. K. (2011, October 3-5). Comparison of voltage stability indexes considering dynamic load. Electrical Power and Energy Conference, Winnipeg, MB, Canada.

Subramani, C., Subhransu, S. D., Kumar, V. & Harish, K. (2012). Implementation of line stability index for contingency analysis and screening in power systems. Journal of Computer Science, 8(4), 585-590.

Saadat, H. (1999). Power System Analysis, New York, USA: McGraw-Hill.

Wordpress.com. (n.d.). World Electrical Engineering. Retrieved May 20, 2020, from https://kalyan07.wordpress.com/2014/1/

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Published

28 December 2021

How to Cite

Mushi, R. J., Boonraksa, T., Paudel, A., & Marungsri, B. (2021). Analysis of Line Instability in Microgrid by Applying Electrical Power Forecasting Approach. Journal of Renewable Energy and Smart Grid Technology, 16(2), 17–30. Retrieved from https://ph01.tci-thaijo.org/index.php/RAST/article/view/227792

Issue

Vol. 16 No. 2 (2021): July-December

Section

Articles

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