Power System Contingency Ranking using Fuzzy Logic Based Approach

Year 2013, Volume: 1 Issue: 2, 24 - 28, 19.06.2013

Almoataz Youssef Abdelaziz , Adel Taha Mahmoud Mostafa Ahmed Hassan

Abstract

Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F) and bus Voltage Magnitude (VM) of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provide the comparison of ranking obtained with Fast Voltage Stability Index (FVSI) method.

Keywords

Contingency Ranking; Fuzzy Sets; Line Flow Index; Fuzzy Inference System.

References

• Andersson, G., et. al (2005). Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance. IEEE Transactions on Power Systems, 20(4), 1922 - 1928.
• Pimjaipong, W., Junrussameevilai, T., Maneerat, N. (2005) Blackout Prevention Plan – The Stability, Reliability and Security Enhancement in Thailand Power Grid. IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, 1-6.
• IEEE Committee Report (1990) Voltage Stability of Power Systems: Concepts, Analytical Tools and Industrial Experiences. IEEE Publication No. 90TH0358- 2-PWR, New York.
• Yu. Q., et. al (2007) Adaptability Evaluation of Transmission Network Planning under Deregulation 42nd International Universities Power Engineering Conference, UPEC 2007, 53-56.
• Greene, S., Dobson, I. and Alvarado, F. L. (1999) Contingency Ranking for Voltage Collapse via Sensitivities from a Single Nose Curve. IEEE Transactions on Power Systems 14(1), 232-239.
• Alammari, R. A. (2002) Fuzzy System Applications for Identification of Weak Buses in Power Systems. Arabian Journal for Science and Engineering 27(2B), 165-178.
• Hong, Y. Y. and Gau, C. H. (1994) Voltage Stability Indicator for Identification of the Weakest Bus Area in Power Systems. IEE Proceedings Generation Transmission Distribution, 144(4), 305-309.
• Musirin I. and Abdul Rahman, T. K. (2002). Estimating maximum loadability of weak bus identification using FVSI. IEEE Power Engineering Review, 22(11), 50-52.
• Chen, Y. L. (1996) Weak Bus-Oriented Optimal Multi-objective VAR Planning. IEEE Transactions on Power Systems 11(4), 1885-1890.
• Song, Y. H. and Wan, H. B. (1997) Kohonen Neural Network based Approach to Voltage Weak Buses/Area Identification IEE Proceedings Generation Transmission Distribution 144(3), 340-344.
• Abdelaziz, A. Y., Abu-Elnaga, M. M., Elsharkawy, M. A. and Elbahrawy, K. M. (2006). Voltage Stability Assessment of Multi-machine Power Systems using Energy Function and Neural Networks Techniques. Electric Power Components and Systems, 34 (12), 1313-1330.
• Musirin, I. (2002). Novel Fast Voltage Stability Index (FVSI) for Voltage Stability Analysis in Power Transmission System. Conference on Research and Development Proceedings, Shah Alam, Malaysia.
• Moghavvemi, M. and Faruque, O. (1998). Real-Time Contingency Evaluation and Ranking Technique. IEE Proceeding on Generation, Transmission and Distribution 145(5), 517-524.
• Cox, L. (1999) The Fuzzy Systems Handbook, 2nd edition, Academic Press, New York.
• Terano, T., Asai, K. and Sugeno, M. (1991) Fuzzy System Theory and its Application, Academic Press, San Diego.
• Shankar, S., Suma, A. P. and Ananthapadmanabha, T. (2009) Fuzzy Approach to Contingency Ranking. International Journal of Recent Trends in Engineering 1(1), 490-494.